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Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We're looking for a Site Reliability Engineer (SRE) to drive the reliability of Tinker end-to-end. You'll work alongside the engineers building the platform and research teams to make every layer of the system more robust and resilient. ABOUT TINKER Tinker https://thinkingmachines.ai/tinker/ is our fine-tuning API that empowers researchers and developers to customize frontier AI to their needs — opening access to capabilities that have previously been concentrated in a handful of labs. We manage the infrastructure while allowing Tinkerers full flexibility in training open weights models with their own data, algorithms, and for their own needs. Tinker is rapidly adding new customers, features, and novel use-cases. We’re hiring to grow the platform alongside the Tinker community. WHAT YOU’LL DO - Define and own end-to-end reliability, from CI/CD flows to production observability and incident response. - Develop appropriate Service Level Objectives for distributed training systems, balancing job completion reliability and scheduling latency with development velocity. - Design and implement monitoring and observability across the full training path. - Drive incident response for Tinker platform issues, ensuring rapid recovery, thorough incident reviews, and systematic improvements that prevent recurrence. - Harden multi-tenant isolation and resource scheduling so that LoRA-based workload co-scheduling maximizes utilization without compromising reliability or data separation - Collaborate with security teams to address production vulnerabilities SKILLS AND QUALIFICATIONS MINIMUM QUALIFICATIONS - Bachelor's degree or equivalent experience in computer science, engineering, or similar. - Experience in distributed systems, cloud infrastructure, or site reliability engineering. - Proficiency writing software to solve reliability problems, including building tooling and automation. - Experience with production incident response, postmortems, and systematic reliability improvement. - Strong communication skills and track record of coordination across engineering and research teams. PREFERRED QUALIFICATIONS - Deep experience operating production cloud services at scale (e.g., public cloud platforms, internal cloud services) - Background in distributed training frameworks and how infrastructure failures surface in training behavior. - Track record building checkpoint and recovery systems for long-running distributed jobs. - Expertise in Kubernetes at scale: deploying, operating, debugging, and tuning clusters handling heterogeneous GPU workloads. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $300,000- $350,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE Evaluation is one of the most important pillars of building frontier AI systems. It guides research direction, powers experimentation, and helps us understand whether changes to data and training are improving the capabilities and behaviors we care about. To support this work, researchers need a powerful, self-serve platform that makes it easy to author evaluations, run them or reproduce them reliably at scale, and extract insight from the results. The platform must support both standardized external benchmarks and fast-moving internal evaluations, many kinds of tasks and graders, and inspection from aggregate metrics down to individual model trajectories. In this role, you will design and build this platform end to end. You will work across Python frameworks, data pipelines, APIs, and user-facing applications, and collaborate closely with pre-training, post-training, and applied teams to improve how we evaluate models and turn results into research decisions. WHAT YOU'LL DO - Design, build, and maintain the platform for authoring, running, tracking, and analyzing model evaluations that are critical in day to day work and model releases. - Work across evaluation libraries, distributed backend systems, data pipelines, APIs, and user-facing applications to deliver capabilities end to end. - Build flexible abstractions for evaluation tasks, environments, graders, datasets, and model outputs without constraining fast-moving research. - Make evaluation results reproducible and trustworthy through versioning, provenance, observability, failure recovery, and robust quality controls. - Partner directly with researchers to identify bottlenecks and turn bespoke workflows into self-serve systems that work across teams. - Work with Research Tooling, the engineering team behind Thinking Machines’ internal research platform. SKILLS AND QUALIFICATIONS MINIMUM QUALIFICATIONS - A bachelor’s degree, or equivalent practical experience, in computer science, engineering, machine learning, or a related field. - Two years of post-grad work experience as a software engineer or ML engineer, exclusive of internships. - Hands-on experience building or maintaining evaluations, benchmarks, graders, or model-quality systems for large language or multimodal models. - Strong software engineering fundamentals and experience building reliable, maintainable systems. - Proficiency in at least one backend programming language; we primarily use Python and Rust. We use React and Typescript on the frontend. - Experience with databases, data pipelines, distributed systems, or other data-intensive infrastructure. - Comfort working across the stack and owning projects from initial problem discovery through deployment and operation. - Experience collaborating with cross-functional partners and subject-matter experts. PREFERRED QUALIFICATIONS We encourage you to apply even if you meet only some of these: - A track record of building frameworks, SDKs, or developer tools with thoughtful abstractions and a strong user experience. - Experience with distributed job execution, workflow orchestration, sandboxed environments, or large-scale data processing. - Experience building polished, intuitive interfaces for inspecting complex data, comparing experiments, or debugging model behavior. - Familiarity with large language or multimodal model evaluation, including model-based grading, human evaluation, or synthetic data. - Experience working closely with researchers to understand their workflows and turn rapidly evolving needs into durable systems. - Experience at a startup or on a small team, building technically complex products end to end. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $300,000 - $350,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We are a team of full stack generalists with strong product instincts who work closely with researchers. We build systems that compound research and engineering velocity over time. We own the internal platform researchers use every day to manage and monitor training runs and evaluations, inspect and debug model trajectories, and compare results on shared leaderboards. You’ll own key parts of this platform, including evaluation and training libraries, experiment-tracking systems, and visualization tools. You’ll identify researchers’ most important bottlenecks and turn them into reliable, generalizable systems. Our team is still small—expect to participate in research meetings, build close relationships with researchers, and gather feedback frequently to develop conviction about where we should invest next. This role requires technical judgment, close cross-functional collaboration, and product intuition. Success means researchers trust your systems to work, rely on them every day, and find them genuinely delightful to use. WHAT YOU’LL DO - Design, build, and maintain research infra, including evaluation frameworks, training systems, experiment tracking platforms, and visualization tools. - Work across backend systems, data pipelines, and user-facing applications to deliver tools end to end. - Partner directly with researchers to identify bottlenecks and unlock new capabilities. Treat research tooling as a product: proactively gather feedback, set priorities, and measure adoption. - Build systems for reproducibility, traceability, and robust quality control across research experiments and model training runs, with monitoring and observability built in. SKILLS AND QUALIFICATIONS MINIMUM QUALIFICATIONS - A bachelor’s degree, or equivalent practical experience, in computer science, engineering, machine learning, or a related field. - Two years of post-grad work experience as a software engineer or ML engineer, exclusive of internships. - Strong software engineering fundamentals and experience building reliable, maintainable systems. - Proficiency in at least one backend programming language; we primarily use Python and Rust. We use React and Typescript on the frontend. - Experience working with databases, data warehouses (Clickhouse), caching systems such as Redis, and other data infra. - Comfort working across the stack and owning projects from initial problem discovery through deployment and operation. - Experience collaborating with cross-functional partners and subject-matter experts. PREFERRED QUALIFICATIONS We encourage you to apply even if you meet only some of these: - A track record of building tools for researchers, improving developer productivity, or creating technical products for technical users. - Experience building polished, intuitive user-facing applications that demonstrate strong product judgment and attention to detail on UI/UX. - Experience at a startup or on a small team, building technically complex products end to end. - Experience building or maintaining ML research infrastructure, such as training frameworks, evaluation libraries, or experiment-tracking systems. - Experience working closely with researchers to understand and solve their tooling needs. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $300,000 - $350,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE This role is responsible for advancing the agentic capabilities of our models, with ownership spanning the full development cycle. Our research team is small, and the role carries a corresponding degree of autonomy and responsibility. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role. WHAT YOU’LL DO - Build a synthetic data framework used across the team, and create new task environments to scale training across agentic capabilities such as tool use, long-horizon tasks, and complex workflows. - Address identified gaps through data and recipe work, validating proposed changes through controlled ablations. - Enhance usability for agent capabilities end to end, translating internal and external feedback into model improvements. SKILLS AND QUALIFICATIONS Minimum qualifications: - Strong engineering skills, ability to contribute code and debug in complex codebases. - Ability to design, run, and interpret experiments with scientific rigor and clarity. - Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales. - Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding. - Clarity in communication, an ability to explain complex technical concepts in writing. Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some: - Experience building synthetic data pipelines and systems that were adopted by others on your team and remain in use today. - Experience owning the end-to-end cycle of identifying gaps in model usability and closing them through custom evaluations and training data. - Experience making large-scale agentic RL infrastructure reliable given the long tail of failures that surface at scale. - Experience improving the agentic capabilities of a frontier model. - PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE - The Coding Agents team makes our models world-class at agentic coding — writing, debugging, and reasoning about code across long-horizon, multi-turn tasks. - You'll join a small, high-leverage team responsible for the recipes, data, and infrastructure behind coding capability gains in every model release. - The team owns the full coding post-training stack: synthetic and human data generation, RL environments and sandboxes, reward and grading design, and large-scale training runs. - This is a research role with real ownership — you'll shape technical direction, not just execute against a spec. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role. WHAT YOU’LL DO - Design and run RL training jobs targeting agentic coding capabilities, iterating on recipes and data. - Build and improve the sandboxed coding environments and reward signals that models are trained and evaluated against. - Generate and curate high-quality synthetic coding data, and build scalable, general-purpose data pipelines. - Design evals that measure real-world coding usefulness, and train models against them to deliver concrete improvements in day-to-day usability. - Debug and analyze large RL runs to catch confounders, reward hacking, and other RL failure modes. - Collaborate closely with infra, evals, and other post-training teams on shared data, joint training runs, and usability improvements — and ship the results into model releases. SKILLS AND QUALIFICATIONS Minimum qualifications: - Strong engineering skills, ability to contribute code and debug in complex codebases. - Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales. - Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding. - Clarity in communication, an ability to explain complex technical concepts in writing. Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some: - Experience building synthetic data pipelines and systems that were adopted by others on your team and remain in use today. - Experience owning the end-to-end cycle of identifying gaps in model usability and closing them through custom evaluations and training data. - Experience making large-scale agentic RL infrastructure reliable given the long tail of failures that surface at scale. - Experience improving the coding capabilities of a frontier model. - PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We’re looking for a researcher to help develop reliable model evaluations for research signals. This role spans evaluation creation, usability, auditing, and efficiency. You’ll work closely with researchers and engineers across post-training and the broader research organization. Depending on your interests and experience, you may focus on one area or work across several of these problems. WHAT YOU’LL DO - Create internal evaluations and research signals for capabilities and behaviors important to model research and post-training. - Develop usability evaluations that measure whether models are genuinely useful in real research and product workflows, and partner with the data flywheel to turn evaluation insights into better data and training signals. - Improve evaluation robustness, including grader reliability, ambiguous ground truth, evaluator disagreement, false positives and negatives, and gaps between measured and intended behavior. - Build benchmark auditing methodologies that help researchers understand, trust, and appropriately use evaluation signals. - Develop specialized agentic evaluation environments and user simulators, including supporting harness development and studying cross-user, cross-harness and cross-environment generalization. - Develop evaluations for personalized preferences, biases, values, and other nuanced dimensions of model behavior in collaboration with post-training crafting. SKILLS AND QUALIFICATIONS Minimum qualifications: - Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding. - Experience designing, building, or analyzing evaluations, benchmarks, datasets, graders, or other measurement systems. - Strong written and verbal communication skills, with the ability to collaborate effectively across research and engineering teams. Preferred qualifications — we encourage you to apply if you meet some but not all of these: - Experience with LLMs, post-training, reinforcement learning, or agentic systems. - Experience with evaluation auditing, human evaluations, LLM-judges, or open-ended task evaluation. - Experience with agentic evaluation, harnesses, long-horizon tasks, or RL environments. - Experience evaluating preferences, personalization, biases, values, or other nuanced model behaviors. - Track record of developing new evaluation methodologies or research signals that meaningfully influenced model development. - Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales. - Strong research judgment: clean ablations, honest baselines, and clear technical writing. - PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

New Energy Nexus

ABOUT NEW ENERGY NEXUS New Energy Nexus (NEX) is an international NGO that supports climate solutions and builds a more equitable clean energy economy. Our laser focus is on clean energy entrepreneurs: providing them with funds, accelerators, and networks to thrive. They are our main audience and our agent of change. We started in California in 2004 and now operate across China, India, Southeast Asia, East and West Africa, and Australia. We have leveraged US$5.4 billion in investment and supported more than 11,000 entrepreneurs. ABOUT THE ROLE & OPPORTUNITY The Director of Development Strategy & Operations will lead the internal engine behind New Energy Nexus' global fundraising and business development efforts. Reporting to the Chief Executive Officer, this role sits at the center of NEX's development team — turning what our development and partnerships staff and country teams learn on the ground with funders into a coherent, prioritized fundraising strategy, and making sure the team is coordinated, resourced, and focused on the highest-value opportunities. This is not primarily a donor-facing relationship management role. It is a strategy, team leadership, and fundraising operations role for someone who understands the business and fund development cycle deeply enough to translate frontline intelligence — what funders are signaling, what's landing and what isn't, where proposals are stalling, which markets and programs are attracting interest — into clear direction for where the team should spend its time. The Director will lead and develop NEX's global platform-level development team, ensuring that individual team members' skills and expertise are deployed effectively against organizational priorities, while bringing together the knowledge and fundraising capabilities of country leaders, chapter teams, and program leads. The ideal candidate has the analytical rigor and systems mindset of a strong operations leader, paired with real fluency in philanthropic and development finance fundraising, so they can be a credible internal leader and partner to a globally distributed team of fundraisers, country directors, and program leads. KEY RESPONSIBILITIES Fundraising Strategy & Prioritization (50%) Help develop a living, prioritized development strategy based on proposal outcomes, funder conversations, and market signals gleaned from team conversations and learnings from fundraisers, country directors, and program leads Synthesize funder conversations, proposal outcomes, market signals and intelligence generated across the organization into clear fundraising priorities Identify where NEX should be focusing time and effort across programs and geographies, based on funder appetite, pipeline strength, and organizational priorities. Maintain a clear view of the funding pipeline across the network — by funder, program, chapter, and stage — and flag risks, gaps, or unrestricted-vs-restricted balance issues early. Translate strategy into concrete guidance: which opportunities to prioritize, which proposals need repositioning, where the team is over- or under-invested. Serve as a thought partner to the CEO and program leads on funding strategy, drawing on patterns across markets rather than any single relationship. Team Coordination & Enablement (35%) Lead and develop the global platform-level development team including recruiting, hiring, onboarding, coaching, and managing performance, and ensure team members' skills are effectively leveraged against NEX's development priorities. Translate the development strategy into a clear rolling work plan for the team, aligning ownership, resources, and timelines. Build strong collaboration across the global development function, bringing together the expertise and fundraising capabilities of platform-level staff, country leaders, fundraisers, and program leads to maximize collective impact. Run the operating rhythm of the fundraising team: pipeline reviews, proposal coordination across programs/chapters/finance/communications, and prioritization check-ins. Coordinate multi-country and multi-program proposals, including combining country and program inputs into a cohesive narrative and budget that aligns with broader NEX operational and growth priorities. Build and own a central library of fundraising collateral - pitch decks, one-pagers, case studies, program summaries, impact data - so materials for funder meetings and conference talks can be assembled quickly rather than built from scratch each time. Draft and maintain core funder and conference decks, adapting a standard set of modular slides/content to each audience so our fundraisers can focus their time on external relationship-building and speaking rather than deck production. Partner with the Chief Communications Officer to keep collateral aligned with current messaging, program updates, and brand standards. Provide country leads and fundraisers with the tools, templates, frameworks, and support to raise funds effectively and report consistently. Build feedback loops so that what is learned through funder and country-level engagement consistently makes its way back into strategy, priorities, and team planning. Systems, Data & Reporting (15%) Accountable for the fundraising CRM and pipeline-tracking systems, ensuring data quality, consistent use across chapters, and useful reporting. Produce regular and ad hoc reporting and dashboards for NEX leadership on pipeline health, funder trends, and progress against platform fundraising goals. Standardize proposal, reporting, and stewardship templates and processes across the global platform-level team. Track outcomes against the fundraising strategy and refine it as new data comes in. Ensure fundraising protocols and internal as well as external reporting are completed on time and to consistent quality standards SUCCESS IN THE FIRST 6–12 MONTHS Success in this role will mean building a more coordinated, strategic, and effective global development function. By the end of the first 6–12 months, the Director will have: Established clear visibility into NEX's global fundraising pipeline, priorities, risks, and opportunities, with leadership able to make decisions based on reliable, current information. Built a strong operating rhythm across the global platform-level development team, with clear ownership, effective collaboration, and team members' skills deployed against the highest-priority fundraising opportunities. Improved coordination across platform, country, and program teams so that high-value proposals are prioritized appropriately, developed collaboratively, and submitted on time and at a consistently high quality. Established consistent CRM and pipeline management practices across the development function, with data being used to inform strategy, prioritization, and reporting. Created a well-organized, accessible library of standardized fundraising collateral and tools that enables fundraisers and country teams to move more quickly and consistently. Established effective feedback loops between funder and country-level intelligence, fundraising performance, and the organization's overall development strategy. Requirements Professional Experience 8+ years of experience across nonprofit fundraising/development, development strategy and/or development operations, including at least 5 years working directly within nonprofit or philanthropic fundraising. Candidates must demonstrate a deep practical understanding of the fundraising cycle, funder decision-making, proposal development and stewardship. Strong track record leading the development of complex philanthropic/grant proposals, ideally involving multiple programmes, geographies or internal stakeholders. Demonstrated experience turning frontline or field-level input into strategy and prioritization. Demonstrated people-management experience, including coaching, performance management, setting priorities and allocating team capacity. Experience leading a distributed team is strongly preferred. Experience operating in a global or matrix organisation, with demonstrated ability to influence and coordinate country leaders, programme teams and other stakeholders who do not report directly to them. Comfortable using CRM and pipeline data to support fundraising decisions, forecasting and team management. Familiarity with the climate, clean energy, or entrepreneurship support landscape is a strong plus. Experience conducting dedicated funder-market or landscape research is advantageous. Personal Attributes Structured, analytical thinker who is comfortable synthesizing messy, qualitative field input into clear recommendations. High level of adaptability in an environment with evolving team structures and workflows, with a genuine comfort operating in ambiguity - this means being a self-starter and embodying a growth mindset. High emotional intelligence — able to lead and support a development team well, build trust with country leads across diverse cultural contexts, and approach unfamiliar markets and norms with genuine curiosity and cultural sensitivity. Exceptional written communication skills — able to distill complex, multi-market fundraising intelligence into concise strategy and proposals. Strong project management skills; able to run a coordination function across concurrent proposals, deadlines, and stakeholders. Comfortable being an internal partner and "connective tissue" role rather than the primary external face of fundraising. Ability to work with a global team across time zones, with periodic travel for internal team meetings and relevant conferences/convenings. Proficiency in database/CRM management, Google Workspace, and Slack. Preferred Qualifications Foundational knowledge of global clean energy and climate change solutions and ecosystems and willingness to engage in in-depth self-learning on any gaps. Experience building or adding structure to a development operations function from an earlier, more informal stage. Due to the global nature of the organization and team, some meetings will take place outside of regular work hours. Benefits BENEFITS AND COMPENSATION This is a full-time remote position. Relocation assistance is not available for this role and compensation is competitive for the market the candidate is based in. Applicable benefits such as medical, dental, vision, paid time off, paid holidays and insurance will depend on the location of the selected candidate. TO APPLY, TIMELINE, AND PROCESS Submit your resume, cover letter and responses to our application questions. Click the “Apply” button below to begin your application. Interview process: Qualified applicants will participate in a series of interviews via Zoom. There will be 2-3 rounds of interviews. Accommodation: If you require accommodations during the interview process, please contact us with your request. New Energy Nexus is an equal employment opportunity employer and does not discriminate against applicants or employees because of race, color, religion, national origin, sex, age, citizenship status, disability status, genetic information, sexual orientation, or gender identity or expression of an otherwise qualified individual, or membership in any other class protected by applicable law. New Energy Nexus hires and promotes individuals on the basis of their qualifications for the job to be filled. We strongly encourage diverse applicants to apply.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We're looking for someone to join our Data team and help researchers get the data they need to train and evaluate our models. You'll work directly with researchers to understand what they need, figure out how to get it, and own the work through delivery. Sometimes that means finding the right vendor. Sometimes it means digging into a new domain, finding unusual sources of data, or working through a request we haven't seen before. This is a hands-on role. You'll spend time talking directly with vendors, reviewing data quality, and building processes and systems that make the work easier to repeat and scale. Our research priorities change quickly, so the work will too. We're looking for someone who is resourceful, flexible, and comfortable figuring things out as they go. WHAT YOU'LL DO - Work with researchers to understand what data they need and turn open-ended requests into a concrete plan. - Own data projects from sourcing and vendor selection through quality review and delivery. - Find the best vendors, experts, and data sources for different types of work. - Build and maintain strong partnerships with data vendors, including international partners. - Review data with researchers and vendors, identify quality issues, and improve the output. - Go deep on new data domains when needed and quickly learn what good looks like. - Build guidelines, processes, and tools that make recurring work easier and more scalable. - Automate repetitive work where it makes sense while staying close to the work that requires judgment. - Work with legal and other teams on practical ways to manage vendor relationships, and adhere to communication guidelines. - Manage multiple projects against research and model timelines and adjust quickly as priorities change. SKILLS AND QUALIFICATIONS Minimum Qualifications: - Experience owning operational, data, product, program, or vendor work from start to finish. - Experience working with data vendors ideally in AI or technology. - Strong problem-solving instincts and a willingness to dig in when there isn't an obvious answer. - Comfortable working directly with researchers or technical teams and turning loosely defined needs into action. - Good judgment about quality and a willingness to inspect the work closely rather than rely only on process. - Strong communication skills across different teams, vendors, and cultures. - Comfortable with ambiguity, changing priorities, and tight timelines. - High initiative and willingness to do whatever part of the job is needed to move the work forward. - Able to add useful structure without overcomplicating things. Preferred qualifications — we encourage you to apply if you meet some but not all of these - Experience with pre-training, post-training, evaluation, annotation, or other AI data types. - Existing relationships with a diverse set of AI or related data vendors. - Experience at an AI company, data company, marketplace, or other fast-moving technology company. - Experience working with international vendors and cross-border operational or compliance issues. - Product, program, operations, or generalist experience in an environment where you had to build the process as you went. - Experience building simple tooling or automation to reduce manual operational work. LOGISTICS - Location: This role is based in San Francisco, CA. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $300,000 - $350,000. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed. As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law. Thinking Machines Lab will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the California Fair Chance Act, the San Francisco Fair Chance Ordinance, and any other applicable state or local fair chance ordinance or law.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE As a safety researcher, you'll work toward ensuring our models are safe and trustworthy. The role sits at the intersection of research and hands-on technical work. A central question is how models come to handle harmful or dual-use requests: what they learn from data, how training shapes where they refuse and where they engage, and what makes those boundaries reliable. You'll explore the science behind these behaviors and design experiments that inform how our models are trained and evaluated. WHAT YOU’LL DO We are hiring across the entire development stack — from pre-training data curation to safety-focused fine-tuning, evaluations, and red-teaming. During project selection we’ll take into account your interests and experience alongside organizational needs. This flexible approach allows us to match talented safety researchers with the teams where they'll have the greatest impact and growth potential. Here are example areas you may contribute to depending on your area of expertise and interest: - Build data filtering pipelines and quality classifiers to shape what models learn from pre-training corpora, and study how those early interventions affect downstream safety behavior. - Apply post-training techniques, including RL from human and AI feedback and policy-based reasoning approaches, to shape how models handle harmful, sensitive, and dual-use requests. - Design, build, and maintain safety evaluations, with particular focus on measuring model behavior on long-horizon and agentic tasks. - Generate and curate synthetic data to train and evaluate models on refusal boundaries and safety-relevant behaviors. - Red-team our models and products to surface failure modes, jailbreaks, and emergent risks before deployment, and design mitigations for what you find. SKILLS AND QUALIFICATIONS Required qualifications: - Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding. - Background in AI safety research, with hands-on experience in at least one area of safety, such as: RLHF/RLAIF, alignment and preference modeling, deliberative alignment, safety evaluations, or red-teaming. - Proficiency in Python and familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX). Comfort debugging distributed training and writing code that scales. - Clarity in communication, an ability to explain complex technical concepts in writing. Preferred qualifications — we encourage you to apply if you meet some but not all of these: - Experience building evaluations for long-horizon, multi-step, or agentic tasks. - Experience generating synthetic data at scale for training or evaluation. - Experience with modern red-teaming/jailbreaking techniques. - Research contributions in AI safety — publications, open-source evaluations, or public red-teaming work. - Familiarity with the AI safety literature and current open problems (e.g., scalable oversight, reward hacking, jailbreak robustness). - PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed. As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE Our team scales reinforcement learning for frontier models. Progress in RL is increasingly set by how well it scales: more rollouts, larger models, and training loops that keep large fleets of accelerators doing useful work. We are particularly interested in people working on high-training-compute, long-horizon RL. We believe the biggest gains come from designing the training recipe and the infrastructure together rather than separately, and we are hiring a researcher who wants to own that boundary. A center of gravity for this role is asynchronous RL. Decoupling generation from training changes both the systems design and the learning problem, and doing it well requires a deep understanding of async RL algorithms, design choices, and trade-offs on both the ML and the systems sides. We expect much of the headroom in RL scaling to come from here. Because generation dominates the cost of RL at scale, good knowledge of inference systems, low-precision numerics, and quantization is recommended: you should be able to reason quantitatively about rollout throughput and cost (batching, KV cache, MoE serving, speculative decoding) and about how inference constraints shape training design. This is a research role with full-stack ownership, from the algorithms to the parallelism plan to the health of the run. WHAT YOU’LL DO - Co-design the RL recipe and the systems that run it: make recipe-level choices jointly with systems-level ones and validate them at frontier scale. - Advance asynchronous RL algorithms. - Improve the efficiency of rollout generation and its integration with training, treating inference as a first-class part of the RL loop. - Run frontier-scale RL end to end: bring up new models and training setups, keep large runs stable and healthy. - Jointly optimize the compute and training efficiency of RL: accelerator utilization, memory, communication, and low-precision numerics. - Do careful empirical science: ablations and scaling studies backed by instrumentation you can trust, written up clearly. SKILLS AND QUALIFICATIONS Minimum qualifications: - Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales. - Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding. - Clarity in communication, an ability to explain complex technical concepts in writing. - Strong research judgment: clean ablations, honest baselines, and clear technical writing. Preferred qualifications — we encourage you to apply if you meet some but not all of these: - PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience. - Strong grounding in RL for large language models, such as modern policy optimization methods and their behavior at scale. - Deep understanding of asynchronous RL: the algorithms, design choices, and trade-offs, on both the ML and the systems sides. - Experience training large models across many accelerators, with comfort inside the distributed stack (parallelism strategies, memory, communication). - Good working knowledge of inference systems: able to reason quantitatively about rollout generation throughput and cost. - Experience building or operating decoupled generation/training RL systems at scale. - Experience with RL on verifiable and agentic tasks, including multi-turn environments. - Experience with RL training stability techniques for large runs. - Familiarity with low-precision training and inference: numerics, quantization, and their implications for RL. - Hands-on work with LLM serving stacks (e.g., SGLang, vLLM, TokenSpeed, or custom engines). - Experience with scaling studies for large models. - Contributions to open-source training or inference frameworks. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed. As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

Flat6Labs

About Flat6Labs: Flat6Labs is the leading entrepreneurship platform in emerging markets, empowering entrepreneurs to build, launch, and grow transformative ideas through acceleration programs, ecosystem development, and tailored innovation services. For more information, visit www.flat6labs.com. About the Project: Nawah is a pre-accelerator program by Flat6Labs , designed to empower early-stage Palestinian startups and entrepreneurs with the knowledge, tools, mentorship, and networks they need to build and grow successful businesses. Through a rigorous, hands-on program, Nawah supports founders in validating their ideas, testing key business assumptions, developing their MVPs, strengthening their go-to-market strategies, and preparing for sustainable growth. The program combines practical workshops, tailored mentorship, expert sessions, and access to a wider network of entrepreneurs and industry leaders. Across multiple cycles, Nawah aims to support a diverse community of Palestinian founders, helping them turn promising ideas into scalable, market-driven startups and connecting them with opportunities, resources, and networks that can accelerate their growth. The program focuses on creating meaningful impact within Palestine’s entrepreneurial ecosystem. Location : Ramallah, Palestine About the Role: Flat6Labs Senior Program Officer, is responsible for supporting Flat6Labs startups in their day-to-day activities as well as supporting the program team in organizing cycle processes, documentation and events. This role would suit a highly proactive, technology savvy and organized individual who is passionate about entrepreneurship and startups and who is inspired to work with startups on a daily basis. Main Responsibilities: Startup Support Support cohort startups throughout the program lifecycle, from pitch and financial model coaching to onboarding, deliverable tracking, and business planning. Program Delivery & Coordination Coordinate program activities end-to-end, including logistics, scheduling, and startup communications. Organize cohort groupings and match mentors, coaches, and speakers to startup needs, following up on session outcomes. Stakeholder & Investor Relations Maintain the program's network of founders, mentors, coaches, and investors, and manage their participation in program events. Data, Feedback & Reporting Track startup progress and KPI data, collect in-cycle feedback, and report insights to the program team. Collect, analyze, and organize startup data to support program decisions. Sourcing & Selection Screen incoming applications and develop sourcing strategies to identify top startups aligned with program criteria. Requirements Minimum 5 years of experience in entrepreneurship, operations or business management. Bachelor's Degree in business management or related discipline. Fluent in English & Arabic. Tech savvy and can manage programs both on the grounds and virtually. Strong project management and organizational skills Excellent communication skills. Engaged and well connected with the local and regional startups ecosystem.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We're looking for a GRC Lead who personally drives our certifications (SOC 2, ISO 27001, FedRAMP and others as we grow) from scoping through audit close, and runs our compliance processes day to day. You'll collect the evidence, write the control documentation, and sit across from the auditor yourself. You'll work closely with security, legal, safety, and engineering to answer compliance and risk questions directly, using your own technical understanding of how our systems work. Day to day, you'll be managing audits, controls, and risk assessments. Alongside that, you'll be building the roadmap for what this function needs to look like in a year. WHAT YOU'LL DO - Own our certification roadmap end to end: scope each certification, build the control set, collect and organize evidence, and represent TML directly to auditors through to close. - Manage recurring compliance processes on a set cadence: control testing, audit prep and response, risk register maintenance, and policy attestations. - Answer compliance and risk questions from engineering, security, and product teams directly, by building enough technical fluency across our infrastructure, model deployment, and data handling to do so without escalating every question. - Track regulatory and framework requirements relevant to an AI company (GDPR, EU AI Act, and similar) and translate them into specific, actionable controls. - Identify gaps in current compliance coverage as the company adds new products, infrastructure, or jurisdictions, and propose what needs to change before it becomes a blocker. - Build and maintain the tooling and documentation that make the next audit cycle faster than the last one. - Plan a multi-quarter roadmap for the GRC function itself, while continuing to personally run the certifications and audits already on the books. SKILLS AND QUALIFICATIONS Minimum qualifications: - 7+ years related experience across technology and cybersecurity Governance, Risk, and Compliance (GRC), with demonstrated breadth across all three disciplines. - Experience leading a SOC 2, ISO 27001, FedRAMP or comparable certification from scoping through audit close. - Hands-on experience collecting audit evidence and writing control documentation. - Experience managing a recurring compliance process, such as control testing, risk register maintenance, or policy attestations. - Experience learning new technical domains quickly and translating them for non-technical stakeholders. Preferred qualifications: We encourage you to apply even if you don't meet all preferred qualifications. - Background as a software engineer or in a technical engineering role, now applied to GRC, evidenced by scripts, tools, or automations you've personally built for evidence collection, control testing, or audit workflows. - Experience translating complex compliance requirements into scalable automation using AI agents and custom built tooling. - Experience growing a GRC function's capability (new certifications, tooling, or processes) as a company scaled. YOU'LL THRIVE IN THIS ROLE IF - You want to run the certification yourself, end to end. - You're the one in the room with the auditor, walking through evidence. - You can hold this week's deadlines and next year's roadmap at the same time. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $225,000 - $350,000. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed. - As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We’re looking for a Safety Operations Lead who will focus on making our products safe by default while supporting fast product iteration and ambitious ideas. You’ll work closely with product engineers, security, researchers, and designers to bake safety and integrity into the way we design, build, and ship human-AI collaboration tools. Day to day, you'll be in the moderation queue while building the tooling and policy that make the next round of moderation faster and more accurate. WHAT YOU’LL DO - Review flagged content, safety escalations, and account-level abuse signals daily. This is a standing responsibility, not a rotation: you'll triage cases, apply policy judgment, and take action (content flags, account review, bans/recovery) on an ongoing basis. - Use patterns from that casework to design and refine safety policy across the product stack, working with engineering, legal, safety research, and security stakeholders. - Build and maintain tooling and automation that make your own casework faster and more consistent: triage agents, ban/recovery workflows, abuse detection frameworks and templates. - Partner with product teams to embed safety into the product experience: model refusals, content flagging, account review, and safety protections, informed by what you're seeing in the queue. - Improve observability and detection for safety-relevant events (model safety trends, abuse patterns, malicious behavior in production), so the next round of cases surfaces faster and with better signal. SKILLS AND QUALIFICATIONS Minimum qualifications: - 7+ years in an operational trust & safety, content moderation, or fraud/abuse ops role with direct, recurring responsibility for a case queue. - Experience owning policy definition, operationalization, and enforcement end to end, evidenced by specific policies or enforcement programs you've built or run. - Direct case experience with at least one of: cybersecurity abuse, CBRN-relevant risk, youth safety, or prompt injection, in a production environment. - Working familiarity with model safety and abuse risk categories (jailbreaks, prompt injection, scaled abuse) and how to identify and mitigate them in a live product, evidenced by specific cases you've handled. - Practical experience using AI tools (Claude, Codex, or similar) to build or accelerate operational workflows, not just as a general user of these tools. Preferred qualifications: We encourage you to apply even if you don’t meet all preferred qualifications. - Experience with safety and integrity operations specifically on AI-powered products or LLM APIs, and their unique abuse patterns. - Track record of turning recurring case patterns into reusable tooling, workflows, or process improvements, while still owning the underlying queue rather than handing it off once the interesting part is solved. - Experience training, onboarding, or setting the quality bar for other moderators or reviewers, showing you scale a team's output rather than just your own. YOU’LL THRIVE IN THIS ROLE IF - You have thoughtful opinions about what safe, trustworthy, frictionless user experiences look like, and you test those opinions against real cases in the queue, not just in the abstract. - You can translate safety and technical constraints into clear product trade-offs and feature requirements, but you see that as something that grows out of daily casework, not a substitute for it. - You bias toward speed and learning, and you measure that bias by how much faster or better the queue runs this month, not by how many new risk categories you've personally discovered. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $250,000 - $300,000. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed. - As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. HR BUSINESS PARTNER Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals. We are a small team of scientists, engineers, and builders who've created some of the most widely used AI products including ChatGPT, Character.ai, and PyTorch. As we scale our team, some of the hardest challenges we face are about empowering and aligning our people and helping our managers make crucial decisions under uncertainty. ABOUT THE ROLE The new role of HR Business Partner will combine two tasks: leadership coaching, and people systems design. The two require different skillsets, but a shared vision for managing talent. You will coach managers at Thinking Machines Lab to be more effective leaders. You will provide strategic support on researcher and engineer performance, team dynamics, and personal growth. You will also build the people infrastructure that will scale this support as the company grows: performance and feedback systems, compensation structures, and career frameworks. WHAT YOU’LL DO - Coach managers by observing how they lead, identifying strengths and blind spots, and working on continuous improvement. - Advise leadership on organizational decisions: team structure, succession planning, and strategic people choices that shape how we work. - Design compensation structures that let us compete for the best machine learning talent in the world while staying aligned on values and principles. - Create career/leveling frameworks that work for a research lab where career advancement often doesn’t mean managing people, contributions such as mentorship and taste are harder to measure, and where senior researchers expect to grow and learn even after a decade in the role. - Build feedback and evaluation processes that help people improve, not just get measured. SKILLS AND QUALIFICATIONS Minimum qualifications: - 5+ years experience working as a people leader or HR business partner in a high-growth technical environment. - Fluency with employment law and best practices in North America. - Proven track record of developing HR practices and supporting a diverse set of talent. Preferred qualifications: We encourage you to apply even if you don’t meet all preferred qualifications. - 10+ years experience as a people leader or HR business partner. - Experience in setting up novel systems that scale in a fast-growing company. - Coaching experience that showcases a skill set in providing confidential support, motivating improvement, and conflict resolution. - Track record of supporting a high trust and low ego environment for high caliber talent with strong retention and growth. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $225,000 - $275,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed. - As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We’re looking for an infrastructure research engineer to design, optimize, and scale the systems that power large AI models. Your work will make inference faster, more cost-effective, more reliable, and more reproducible to enable our teams to focus on advancing model capabilities rather than managing bottlenecks. Our focus is on performant and efficient model inference both to power real-world applications and to accelerate research. This role is responsible for the infrastructure that ensures every experiment, evaluation, and deployment runs smoothly at scale. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role. WHAT YOU’LL DO - Work alongside researchers and engineers to bring cutting-edge AI models into production. - Collaborate with research teams to enable high-performance inference for novel architectures. - Design and implement new techniques, tools, and architectures that improve performance, latency, throughput, and efficiency. - Optimize our codebase and compute fleet (e.g., GPUs) to fully utilize hardware FLOPs, bandwidth, and memory. - Extend orchestration frameworks (e.g., Kubernetes, Ray, SLURM) for distributed inference, evaluation, and large-batch serving. - Establish standards for reliability, observability, and reproducibility across the inference stack. - Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure. SKILLS AND QUALIFICATIONS Minimum qualifications: - Bachelor’s degree or equivalent experience in computer science, engineering, or similar. - Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures. - Experience with inference serving systems optimized for throughput and latency (e.g., SGLang, vLLM). - Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts. - A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships. - Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases Preferred qualifications — we encourage you to apply if you meet some but not all of these: - Experience training or supporting large-scale language models with hundreds of billions of parameters or more. - Understanding of distributed compute systems, GPU parallelism, and hardware-aware optimizations. - Contributions to open-source ML or systems infrastructure projects (e.g., SGLang, vLLM, PyTorch, Triton, DeepSpeed, XLA). - Track record of improving research productivity through infrastructure design or process improvements. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We’re looking for generalist infrastructure and systems engineers to help build the systems that power our foundation models and the internal teams on research and product development to be able to create the models and ship the products powered by our models. You'll join a small, high-impact team responsible for architecting and scaling the core infrastructure behind everything we do. You’ll work across the full technical stack, solving complex distributed systems problems and building robust, scalable platforms. Infrastructure is critical to us: it's the bedrock that enables every breakthrough. You'll work directly with researchers to accelerate experiments, improve infrastructure efficiency, and enable key insights across our models, products, and data assets. WHAT YOU’LL DO We interview generally, but during project selection we’ll take into account your interests and experience alongside organizational needs. This flexible approach allows us to match talented engineers with the infrastructure teams where they'll have the greatest impact and growth potential. Here are example areas you may contribute to depending on your area of expertise and interest: - Core Infrastructure: We support teams that train, research, and ultimately serve AI models and build the underlying infrastructure for the clusters to reliably and safely train frontier models. Examples might include building systems and running large Kubernetes clusters with GPU workloads, or building infrastructure to support Tinker. - Data Infrastructure: We build and maintain the data systems for our research and products. You'll design and optimize data pipelines using tools like Spark and other modern data infrastructure technologies. You’ll build scalable, reliable, data infrastructure while embedding governance best practices. - Developer Productivity: We care deeply about research and engineering productivity and our ability to continue shipping quickly. We build tooling, systems, frameworks, and systems to make sure everyone gets well configured, optimized developer environments. SKILLS AND QUALIFICATIONS MINIMUM QUALIFICATIONS - Bachelor’s degree or equivalent experience in computer science, engineering, or similar. - Proficiency in at least one backend language (we use Python or Rust). - Experience operating large‑scale clusters and container orchestration systems (e.g. Kubernetes or Slurm). - Comfort operating across the stack and owning projects end-to-end. - Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts. - A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships. PREFERRED QUALIFICATIONS - Strong debugging across application, OS, and network layers. - Proficiency in Python or Rust (or similar), containers, and modern CI. - Experience with Kubernetes, controllers/operators, or performance profiling. - Familiarity with GPU/ML workflows or large‑scale data/eval pipelines. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $300,000 - $400,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We're hiring an Engineering Manager to lead a team of senior and staff-level engineers across ML infrastructure and product. You will help the team build and scale systems that are reliable, performant, and easy to operate. This role combines collaboration with hand-on work. You’ll partner with tech leads to set the technical direction for your team and own its execution. You should also be ready to go deep on system design and contribute directly when needed. WHAT YOU’LL DO - Lead and grow a team of senior and staff-level engineers, setting clear expectations and maintaining a high bar for execution. - Own architecture, system design, and long-term technical direction for your team's systems, with emphasis on reliability and performance. - Contribute directly to design reviews, prototyping, and debugging critical issues. - Partner with researchers and product teams to define roadmaps and prioritize work. - Hire and close senior engineering talent. Mentor engineers into technical leaders. SKILLS AND QUALIFICATIONS MINIMUM QUALIFICATIONS - Bachelor’s degree or equivalent industry experience in computer science, engineering, or similar. - 8+ years of experience building and scaling production systems, including system design and distributed systems. - 3+ years of engineering management experience in high-growth environments. PREFERRED QUALIFICATIONS - Experience managing teams of senior or staff-level engineers. - Background in infrastructure, systems engineering, or developer productivity. - Familiarity with AI/ML systems, data infrastructure, or high-performance computing. - Track record of building or contributing to widely used systems, platforms, or tools. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $450,000 - $500,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE The role of pre-training researchers sits at the core of our roadmap. This work blends research with large-scale data engineering to help assemble the pre-training datasets and data systems that underpin the next generation of AI models. You’ll design and implement methods for sourcing, curating, and analyzing pre-training data for quality and performance. You’ll work with automated pipelines and human-in-the-loop processes, contributing both scientific insight and production-grade code. It’s ideal for someone who enjoys working at the intersection of data, machine learning, and systems, and who’s excited by the challenge of shaping frontier AI. This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role. WHAT YOU’LL DO - Design and implement techniques for curating, sourcing, and filtering large-scale text, code, and multimodal data. - Develop data quality metrics and analysis to measure coverage, diversity, and representativeness across sources. - Collaborate with research and infrastructure teams to scale data processing systems efficiently and reproducibly. - Investigate and mitigate data risks, including privacy, safety, and licensing concerns, to ensure responsible and ethical data use. - Continuously evaluate dataset improvements by analyzing their downstream effects on model learning and behavior. - Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia. SKILLS AND QUALIFICATIONS Minimum qualifications: - Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales. - Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding. - Clarity in communication, an ability to explain complex technical concepts in writing. Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some: - A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs. - Experience with curation, preprocessing, and analysis of large-scale text, code, or multimodal datasets. - Prior experience in data engineering, dataset construction, or large-scale web data processing for machine learning models. - Experience evaluating or improving training data quality and knowledge of data ethics, safety, and licensing frameworks relevant to AI dataset creation. - Contributions to open datasets, research publications, or data tooling. - PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We're hiring a Recruiting Sourcer to build the pipeline of exceptional research and engineering talent that will define the next generation of AI. You'll partner closely with recruiters, hiring managers, and our technical leaders to identify, engage, and build relationships with candidates across research, engineering, and infrastructure. This role is foundational to our growth. You'll need to think creatively about where to find rare technical talent, craft outreach that resonates with researchers and engineers who aren't actively looking, and help us build a sourcing function from the ground up at a fast-moving startup. WHAT YOU’LL DO - Build and manage a pipeline of candidates across research, engineering, and infrastructure roles, using creative and diverse sourcing channels - Partner with recruiters and hiring managers to deeply understand role requirements and translate them into effective sourcing strategies - Write and send personalized outreach that engages passive candidates, including senior researchers and engineers - Track pipeline metrics and sourcing effectiveness, and iterate on strategy based on what's working - Represent Thinking Machines' mission and culture authentically to prospective candidates throughout the sourcing process - Help build and refine our sourcing tools, processes, and infrastructure as the team scales SKILLS AND QUALIFICATIONS Minimum qualifications: - 3+ years of sourcing or recruiting experience, ideally supporting technical roles in AI labs or tech startups - Track record of successfully identifying and engaging passive technical candidates, including researchers and engineers - Excellent written communication skills, with the ability to craft compelling, personalized outreach Preferred qualifications: - Familiarity with the AI/ML research landscape, including key labs, conferences, and communities - Experience using sourcing tools and platforms (e.g., LinkedIn Recruiter, GitHub, academic search tools) - Experience building sourcing processes and infrastructure at an early-stage or fast-scaling company - Comfort operating with significant autonomy and adapting quickly as priorities shift LOGISTICS - Location: This role is based in San Francisco, CA. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $200,000 - $275,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE Thinking Machines builds multimodal-first. For us, there is no separate multimodal work. It’s at the core of everything we do, from the scientific goals we’re setting to the infrastructure we’re building. We’re looking for researchers to advance the frontier of audio capabilities. You’ll explore how audio models enable more natural and efficient communication/collaboration, preserving more information and capturing user intent. This is a highly collaborative role. You’ll work closely across pre-training, post-training, and product with world-class researchers, infrastructure engineers, and designers. This is an opportunity to shape the fundamental capabilities of AI systems that millions of people will use. This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role. WHAT YOU’LL DO - Own research projects on audio training, low-latency inference and conversational responsiveness. - Design and train large-scale models that natively support audio input and output. - Investigate scaling behavior such as how data, model size, and compute affect capability and efficiency. - Build and maintain audio data pipelines, including preprocessing, filtering, segmentation, and alignment for training and evaluation. - Collaborate with data and infrastructure teams to scale audio training efficiently across distributed systems. - Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia. SKILLS AND QUALIFICATIONS Minimum qualifications: - Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor. - Understanding of machine learning fundamentals, large-scale training, and distributed compute environments. - Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales. - Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding. - Clarity in communication, an ability to explain complex technical concepts in writing. Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some: - A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs. - Experience with real-time inference, streaming architectures, or optimization for low latency. - Prior experience training or evaluating large-scale audio or multimodal models. - Publications, releases, or open-source projects related to speech, audio, voice, or similar areas. - Demonstrated experience in audio or speech modeling, including ASR, TTS, or self-supervised audio learning. - PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We’re looking for an infrastructure research engineer to design and build the core systems that enable efficient large-scale model training with a focus on numerics. You will focus on improving the numerical foundations of our distributed training stack, from precision formats and kernel optimizations to communication frameworks that make training trillion-parameter models stable, scalable, and fast. This role is ideal for someone who thrives at the intersection of research and systems engineering: a builder who understands both the math of optimization and the realities of distributed compute. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role. WHAT YOU’LL DO - Design and optimize distributed training infrastructure for large-scale LLMs, focusing on performance, stability, and reproducibility across multi-GPU and multi-node setups. - Implement and evaluate low-precision numerics (for example, BF16, MXFP8, NVFP4) to improve efficiency without sacrificing model quality. - Develop kernels and communication primitives that use hardware-level support for mixed and low-precision arithmetic. - Collaborate with research teams to co-design model architectures and training recipes that align with emerging numeric formats and stability constraints. - Prototype and benchmark scaling strategies such as data, tensor, and pipeline parallelism that integrate precision-adaptive computation and quantized communication. - Contribute to the design of our internal orchestration and monitoring systems to ensure that thousands of distributed experiments can run efficiently and reproducibly. - Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure. SKILLS AND QUALIFICATIONS Minimum qualifications: - Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar. - Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures. - Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts. - A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships. - Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases in areas such as floating-point numerics, low-precision arithmetic, and distributed systems. Preferred qualifications — we encourage you to apply if you meet some but not all of these: - Familiarity with distributed frameworks such as PyTorch/XLA, DeepSpeed, Megatron-LM. - Experience implementing FP8, INT8, or block-floating point (MX) formats and understanding their numerical trade-offs. - Prior contributions to open-source deep learning infrastructure such as PyTorch, DeepSpeed, or XLA. - Publications, patents, or projects related to numerical optimization, communication-efficient training, or systems for large models. - Experience training and supporting large-scale AI models. - Track record of improving research productivity through infrastructure design or process improvements. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE Thinking Machines builds multimodal-first. We’re looking for new team members to advance the science of visual perception and multimodal learning. We think about how vision and language interact at scale. We design architectures that fuse pixels and text, build datasets and evaluation methods that test real-world comprehension, and develop representations that let models ground abstract concepts in the physical world. Our goal is to create multimodal systems that support seamless integration into real-world environments. You’ll work at the intersection of visual understanding, multimodal reasoning, and large-scale model training. You’ll help develop the architectures, data, and evaluation tools that teach AI to see, understand, and collaborate. The best candidate is curious about multimodal interfaces, has experience running large scale experiments and is comfortable contributing to complex engineering systems. While we are looking for a person with expertise in multimodality, Thinking Machines Lab operates in a unified fashion and expects new hires to work across modalities as one team. This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role. WHAT YOU’LL DO - Own research projects on training and performance analysis of multimodal AI models. - Curate and build large-scale datasets and evaluation benchmarks to advance vision capabilities. - Work with our data infrastructure engineers, pretraining researchers and engineers, and product team to create frontier multimodal models and the products that leverage them. - Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia. SKILLS AND QUALIFICATIONS Minimum qualifications: - Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor. - Understanding of machine learning fundamentals, large-scale training, and distributed compute environments. - Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales. - Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding. - Clarity in communication, an ability to explain complex technical concepts in writing. Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some: - Research or engineering contributions in visual reasoning, spatial understanding, or multimodal architecture design. - Experience developing evaluation frameworks for multimodal tasks. - Publications or open-source contributions in vision-language modeling, video understanding, or multimodal AI. - A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs. - PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000- $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE As Product Manager for Deployment, you will own how Thinking Machines' models and fine-tuned checkpoints go from training into production use. You will shape the path from a trained model to a served, reliable, cost-effective endpoint — covering inference infrastructure, serving APIs, latency and throughput tradeoffs, scaling behavior, observability, and the workflows researchers and external users rely on to deploy their work with confidence. This is not a mature MLOps role at an established platform. Deployment at Thinking Machines is still being defined: what "production-ready" means for a fine-tuned model, which serving paths we support, how much control users get over performance and cost tradeoffs, and how we scale reliably as usage grows. You will work from infrastructure capability through to a deployment experience that is fast, predictable, and trustworthy. The strongest candidate has shipped and operated production ML or infrastructure systems before, ideally as an engineer before becoming a product leader, and can reason from strategy down to autoscaling behavior, latency budgets, rollout safety, and the on-call realities of running models in production. What You'll Do - Own deployment strategy, roadmap, and success metrics for taking models and Tinker-trained checkpoints into production, in close partnership with infrastructure, research, engineering, and GTM - Define priority deployment paths and workflows across model serving, autoscaling, versioning, rollback, monitoring, and incident response - Work at engineering depth on serving architecture, latency and cost tradeoffs, reliability targets, capacity planning, and API/SDK surfaces for deployment - Build direct feedback loops with users deploying models in production, and turn scattered signals into a clear view of what's broken, what's missing, and what to prioritize next - Drive ambiguous workstreams end to end: technical scoping, dependency resolution, launch readiness, on-call/escalation design, and post-incident learning - Connect deployment decisions to the model and infrastructure roadmap, making visible the tradeoffs between flexibility, reliability, and operational cost - Shape SLAs, pricing/packaging inputs for hosted inference, and the operating model for a deployment platform expected to scale quickly - Do whatever work makes deployment succeed — reviewing a serving config, joining an incident retro, inspecting latency data, or writing the rollout plan for a new model Skills and Qualifications - Experience owning a production ML serving, infrastructure, or deployment product, with direct involvement in reliability, scaling, or performance decisions - Track record working at engineering depth with production systems — comfortable discussing latency, throughput, autoscaling, rollback, or incident response in specifics - Experience taking a technical product from early usage through to reliable, scaled production use Preferred qualifications: - Background as an engineer or technical founder before moving into product leadership - Experience with ML inference infrastructure specifically (model serving frameworks, GPU scheduling, batching, quantization tradeoffs, or similar) - Experience operating in a startup, lab, or new product area where the deployment model and roadmap weren't handed to you - Comfortable moving between a strategic narrative and a specific technical detail (an autoscaling policy, an SLA definition, a rollout gate) without losing judgment - Experience building trust with technical users through evidence, responsiveness, and follow-through rather than process ownership Logistics - Location: This role is based in San Francisco, CA. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $400,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Thinking Machines Lab

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We’re looking for an engineer to design, build, and operate the GPU supercomputing environment that powers large‑scale training and inference. You will deliver high‑performant, reliable, and cost‑efficient compute so our users and researchers can move fast at scale. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role. WHAT YOU’LL DO - Operate and automate large GPU clusters including provisioning, imaging, and capacity planning. - Write software that abstracts cluster management and presents a unified interface for training and inference. - Extend scheduling/orchestration (Kubernetes, Slurm, or similar) for topology‑aware placement, preemption, quotas, and fair‑share multi‑tenancy. - Monitor and improve operational metrics of speed, reliability, and error recovery. - Build reliable storage and artifact paths for datasets, checkpoints, and logs with clear retention and lineage. - Partner with researchers to unblock scale runs and advise on parallelism and performance trade‑offs. SKILLS AND QUALIFICATIONS Minimum qualifications: - Bachelor’s degree or equivalent experience in computer science, engineering, or similar. - Proficiency in at least one backend language (we use Python or Rust). - Experience operating large‑scale clusters and container orchestration systems (e.g. Kubernetes or Slurm). - Comfort operating across the stack and owning projects end-to-end. - Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts. - A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships. Preferred qualifications — we encourage you to apply if you meet some but not all of these: - Strong systems background: Linux, networking, and infrastructure‑as‑code. - Familiarity with CUDA/NCCL and performance profiling for distributed training/inference. - Prior work supporting large‑scale model training or inference environments. - Understanding of deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and their underlying system architectures. - Track record of working in fast-paced environments balancing care with urgency. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.