8549c317-12f Senior Research Scientist, Reward Models As a Senior Research Scientist on our Reward Models team, you'll lead research efforts to improve how we specify and learn human preferences at scale.

Your work will directly shape how our models understand and optimize for what humans actually want , enabling Claude to be more useful, more reliable, and better aligned with human values.

This role focuses on pushing the frontier of reward modeling for large language models. You'll develop novel architectures and training methodologies for RLHF, research new approaches to LLM-based evaluation and grading (including rubric-based methods), and investigate techniques to identify and mitigate reward hacking.

You'll collaborate closely with teams across Anthropic, including Finetuning, Alignment Science, and our broader research organization, to ensure your work translates into concrete improvements in both model capabilities and safety.

We're looking for someone who can drive ambitious research agendas while also shipping practical improvements to production systems. You'll have the opportunity to work on some of the most important open problems in AI alignment, with access to frontier models and significant computational resources.

Your work will directly advance the science of how we train AI systems to be both highly capable and safe.

Responsibilities:

  • Lead research on novel reward model architectures and training approaches for RLHF
  • Develop and evaluate LLM-based grading and evaluation methods, including rubric-driven approaches that improve consistency and interpretability
  • Research techniques to detect, characterize, and mitigate reward hacking and specification gaming
  • Design experiments to understand reward model generalization, robustness, and failure modes
  • Collaborate with the Finetuning team to translate research insights into improvements for production training pipelines
  • Contribute to research publications, blog posts, and internal documentation
  • Mentor other researchers and help build institutional knowledge around reward modeling

You may be a good fit if you:

  • Have a track record of research contributions in reward modeling, RLHF, or closely related areas of machine learning
  • Have experience training and evaluating reward models for large language models
  • Are comfortable designing and running large-scale experiments with significant computational resources
  • Can work effectively across research and engineering, iterating quickly while maintaining scientific rigor
  • Enjoy collaborative research and can communicate complex ideas clearly to diverse audiences
  • Care deeply about building AI systems that are both highly capable and safe

Strong candidates may also:

  • Have published research on reward modeling, preference learning, or RLHF
  • Have experience with LLM-as-judge approaches, including calibration and reliability challenges
  • Have worked on reward hacking, specification gaming, or related robustness problems
  • Have experience with constitutional AI, debate, or other scalable oversight approaches
  • Have contributed to production ML systems at scale
  • Have familiarity with interpretability techniques as applied to understanding reward model behavior

The annual compensation range for this role is $350,000-$500,000 USD.

Logistics:

  • Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
  • Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
  • Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links,visit anthropic.com/careers directly for confirmed position openings.

How we're different:

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

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]]> full-time senior hybrid $350,000-$500,000 USD reward modeling, RLHF, large language models, novel architectures, training methodologies, evaluation and grading, rubric-based methods, reward hacking, specification gaming, generalization, robustness, failure modes, computational resources, scientific rigor, communication skills, interpretability techniques Engineering Technology Anthropic https://logos.yubhub.co/anthropic.com.png Anthropic is a public benefit corporation that aims to create reliable, interpretable, and steerable AI systems. https://www.anthropic.com/ https://job-boards.greenhouse.io/anthropic/jobs/5024835008 Remote-Friendly (Travel Required) | San Francisco, CA 2026-04-18 dc6154f8-cff Research Engineer, Pretraining Scaling - London About Anthropic\n\nAnthropic's mission is to create reliable, interpretable, and steerable AI systems.\n\nAbout the Role:\n\nAs a Research Engineer on Anthropic's ML Performance and Scaling team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.\n\nResponsibilities:\n\n- Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability\n- Debug and resolve complex issues across the full stack,from hardware errors and networking to training dynamics and evaluation infrastructure\n- Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance\n- Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams\n- Build and maintain production logging, monitoring dashboards, and evaluation infrastructure\n- Add new capabilities to the training codebase, such as long context support or novel architectures\n- Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams\n- Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned\n\nYou May Be a Good Fit If You:\n\n- Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems\n- Genuinely enjoy both research and engineering work,you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other\n- Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure\n- Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs\n- Excel at debugging complex, ambiguous problems across multiple layers of the stack\n- Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents\n- Are passionate about the work itself and want to refine your craft as a research engineer\n- Care about the societal impacts of AI and responsible scaling\n\nStrong Candidates May Also Have:\n\n- Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale\n- Contributed to open-source LLM frameworks (e.g., open_lm, llm-foundry, mesh-transformer-jax)\n- Published research on model training, scaling laws, or ML systems\n- Experience with production ML systems, observability tools, or evaluation infrastructure\n- Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence\n\nWhat Makes This Role Unique:\n\nThis is not a typical research engineering role. The work is highly operational,you'll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.\n\nHowever, this operational intensity comes with extraordinary learning opportunities. You'll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You'll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can't be easily transferred. For people who thrive on this type of work, it's uniquely rewarding.\n\nWe're building a close-knit team of people who genuinely care about doing excellent work together. If you're someone who wants to be part of training the models that will define the future of AI,and you're excited about the full reality of what that entails,we'd love to hear from you.\n\nLocation:\n\nThis role requires working in-office 5 days per week in London.\n\nDeadline to apply:\n\nNone. Applications will be reviewed on a rolling basis.\n\nThe annual compensation range for this role is listed below.\n\nFor sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n\nAnnual Salary:\n\n£260,000-£630,000 GBP\n\nLogistics\n\nMinimum education:\n\nBachelor’s degree or an equivalent combination of education, training, and/or experience\n\nRequired field of study:\n\nA field relevant to the role as demonstrated through coursework, training, or professional experience\n\nMinimum years of experience:\n\nYears of experience required will correlate with the internal job level requirements for the position\n\nLocation-based hybrid policy:\n\nCurrently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n\nVisa sponsorship:\n\nWe do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n\nWe encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.\n\nYour safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links,visit anthropic.com/careers directly for confirmed position openings.\n\nHow we're different\n\nWe believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the h

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]]> full-time senior onsite £260,000-£630,000 GBP JAX, TPU, PyTorch, large-scale distributed systems, model operations, performance optimization, observability, reliability, debugging, complex issues, hardware errors, networking, training dynamics, evaluation infrastructure, experiments, training efficiency, step time, uptime, model performance, production logging, monitoring dashboards, codebase, long context support, novel architectures, collaboration, institutional knowledge, documentation, debugging approaches, lessons learned Engineering Technology Anthropic https://logos.yubhub.co/anthropic.com.png Anthropic is a technology company focused on developing artificial intelligence systems. https://www.anthropic.com/ https://job-boards.greenhouse.io/anthropic/jobs/4938436008 London, UK 2026-04-18 6960fd5f-0e8 Research Engineer, Pretraining Scaling About the Role:\n\nAs a Research Engineer on Anthropic's ML Performance and Scaling team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.\n\n## Responsibilities:\n\n- Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability\n- Debug and resolve complex issues across the full stack,from hardware errors and networking to training dynamics and evaluation infrastructure\n- Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance\n- Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams\n- Build and maintain production logging, monitoring dashboards, and evaluation infrastructure\n- Add new capabilities to the training codebase, such as long context support or novel architectures\n- Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams\n- Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned\n\n## You May Be a Good Fit If You:\n\n- Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems\n- Genuinely enjoy both research and engineering work,you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other\n- Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure\n- Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs\n- Excel at debugging complex, ambiguous problems across multiple layers of the stack\n- Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents\n- Are passionate about the work itself and want to refine your craft as a research engineer\n- Care about the societal impacts of AI and responsible scaling\n\n## Strong Candidates May Also Have:\n\n- Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale\n- Contributed to open-source LLM frameworks (e.g., open_lm, llm-foundry, mesh-transformer-jax)\n- Published research on model training, scaling laws, or ML systems\n- Experience with production ML systems, observability tools, or evaluation infrastructure\n- Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence\n\n## What Makes This Role Unique:\n\nThis is not a typical research engineering role. The work is highly operational,you'll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.\n\nHowever, this operational intensity comes with extraordinary learning opportunities. You'll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You'll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can't be easily transferred. For people who thrive on this type of work, it's uniquely rewarding.\n\nWe're building a close-knit team of people who genuinely care about doing excellent work together. If you're someone who wants to be part of training the models that will define the future of AI,and you're excited about the full reality of what that entails,we'd love to hear from you.\n\nLocation: This role requires working in-office 5 days per week in San Francisco.\n\nDeadline to apply: None. Applications will be reviewed on a rolling basis.\n\nThe annual compensation range for this role is listed below.\n\nFor sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n\nAnnual Salary: $350,000-$850,000 USD\n\n## Logistics\n\nMinimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n\nRequired field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience\n\nMinimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n\nLocation-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n\nVisa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n\nWe encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.\n\nYour safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links,visit anthropic.com/careers directly for confirmed position openings.\n\n## How we're different\n\nWe believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing

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]]> full-time senior onsite $350,000-$850,000 USD JAX, TPU, PyTorch, large-scale distributed systems, model operations, performance optimization, observability, reliability, debugging, complex issues, hardware errors, networking, training dynamics, evaluation infrastructure, experiments, training efficiency, step time, uptime, model performance, production logging, monitoring dashboards, new capabilities, long context support, novel architectures, collaboration, institutional knowledge, documentation, debugging approaches, lessons learned Engineering Technology Anthropic https://logos.yubhub.co/anthropic.com.png Anthropic is a technology company that focuses on developing artificial intelligence systems. https://www.anthropic.com https://job-boards.greenhouse.io/anthropic/jobs/4938432008 San Francisco, CA 2026-04-18 4e51470c-8f1 Software Engineer, Accelerators Software Engineer, Accelerators

Location

San Francisco

Employment Type

Full time

Department

Scaling

Compensation

  • $295K – $380K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

Benefits

  • Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
  • Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
  • 401(k) retirement plan with employer match
  • Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
  • Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
  • 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
  • Mental health and wellness support
  • Employer-paid basic life and disability coverage
  • Annual learning and development stipend to fuel your professional growth
  • Daily meals in our offices, and meal delivery credits as eligible
  • Relocation support for eligible employees
  • Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

The Kernels team at OpenAI builds the low-level software that accelerates our most ambitious AI research.

We work at the boundary of hardware and software, developing high-performance kernels, distributed system optimizations, and runtime improvements to make large-scale training and inference more efficient.

Our work enables OpenAI to push the limits by ensuring models - from LLMs to recommender systems - to run reliably on advanced supercomputing platforms. That includes adapting our software stack to new types of accelerators, tuning system performance end-to-end, and removing bottlenecks across every layer of the stack.

About the Role

On the Accelerators team, you will help OpenAI evaluate and bring up new compute platforms that can support large-scale AI training and inference.

Your work will range from prototyping system software on new accelerators to enabling performance optimizations across our AI workloads.

You’ll work across the stack, collaborating with both hardware and software aspects - working on kernels, sharding strategies, scaling across distributed systems, and performance modeling.

You'll help adapt OpenAI's software stack to non-traditional hardware and drive efficiency improvements in core AI workloads. This is not a compiler-focused role, rather bridging ML algorithms with system performance - especially at scale.

In this role, you will:

  • Prototype and enable OpenAI's AI software stack on new, exploratory accelerator platforms.
  • Optimize large-scale model performance (LLMs, recommender systems, distributed AI workloads) for diverse hardware environments.
  • Develop kernels, sharding mechanisms, and system scaling strategies tailored to emerging accelerators.
  • Collaborate on optimizations at the model code level (e.g. PyTorch) and below to enhance performance on non-traditional hardware.

Perform system-level performance modeling, debug bottlenecks, and drive end-to-end optimization.

  • Work with hardware teams and vendors to evaluate alternatives to existing platforms and adapt the software stack to their architectures.
  • Contribute to runtime improvements, compute/communication overlapping, and scaling efforts for frontier AI workloads.

You might thrive in this role if you have:

  • 3+ years of experience working on AI infrastructure, including kernels, systems, or hardware-software co-design
  • Hands-on experience with accelerator platforms for AI at data center scale (e.g., TPUs, custom silicon, exploratory architectures).
  • Strong understanding of kernels, sharding, runtime systems, or distributed scaling techniques.
  • Familiarity with optimizing LLMs, CNNs, or recommender models for hardware efficiency.
  • Experience with performance modeling, system debugging, and software stack adaptation for novel architectures.
  • Exposure to mobile accelerators is welcome, but experience enabling data center-scale AI hardware is preferred.
  • Ability to operate across multiple levels of the stack, rapidly prototype solutions, and navigate ambiguity in early hardware bring-up phases
  • Interest in shaping the future of AI compute through exploration of alternatives to mainstream accelerators.

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]]> full-time senior onsite $295K – $380K • Offers Equity AI infrastructure, kernels, systems, hardware-software co-design, accelerator platforms, TPUs, custom silicon, exploratory architectures, kernels, sharding, runtime systems, distributed scaling techniques, LLMs, CNNs, recommender models, hardware efficiency, performance modeling, system debugging, software stack adaptation, novel architectures, mobile accelerators, data center-scale AI hardware Engineering Technology OpenAI https://logos.yubhub.co/openai.com.png OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. They push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through their products. https://jobs.ashbyhq.com https://jobs.ashbyhq.com/openai/f386b209-1259-4b79-bf5a-aa97fc7ce77b San Francisco 2026-03-06 1ee94df2-ca6 Senior Research Engineer/Scientist - On-Device Transformer Models Location

San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

Consumer Products

Compensation

  • $380K – $445K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

  • Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
  • Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
  • 401(k) retirement plan with employer match
  • Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
  • Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
  • 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
  • Mental health and wellness support
  • Employer-paid basic life and disability coverage
  • Annual learning and development stipend to fuel your professional growth
  • Daily meals in our offices, and meal delivery credits as eligible
  • Relocation support for eligible employees
  • Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Future of Computing Research team is an Applied Research team within the Consumer Products group focused on developing new methods and models to support our vision for the future of computing as we advance forward in our mission of building AGI that benefits all of humanity.

About the Role

As a Research Engineer/Scientist on the Future of Computing Research team, you will work together with _both_ the best ML researchers in the world and the greatest design talent of our generation to push the frontier of model capabilities.

This role is based in San Francisco, CA. We follow a hybrid model with 4 days a week in the office and offer relocation assistance to new employees.

In this role you will:

  • Train and evaluate multimodal SoTA models along axis that are important to our vision for future devices.
  • Develop novel architectures that improve model performance when scaling the models themselves is not an option.
  • Run through the necessary walls to take nascent research capabilities and turn them into capabilities we can build on top of.

You might thrive in this role if you:

  • Have a research background related to developing on-device transformer models.
  • Love performance optimization and working with GPU kernel engineers (but you do not need CUDA experience yourself).
  • Do rigorous science (rather than vibes based). We need confidence in the experiments we run to move quickly.
  • Have already spent time in the weeds teaching models to speak and perceive.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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]]> full-time senior hybrid $380K – $445K • Offers Equity research background related to developing on-device transformer models, performance optimization, GPU kernel engineers, rigorous science, teaching models to speak and perceive, CUDA experience, multimodal SoTA models, novel architectures, nascent research capabilities Engineering Technology OpenAI https://logos.yubhub.co/openai.com.png OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. The company was founded in 2015 and has since grown to become a leading player in the field of artificial intelligence. https://jobs.ashbyhq.com https://jobs.ashbyhq.com/openai/7f9eb43b-423e-43e4-9f42-d14b8ba0f234 San Francisco 2026-03-06