You will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems.

Responsibilities

• Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development
• Independently lead small research projects while collaborating with team members on larger initiatives
• Design, run, and analyze scientific experiments to advance our understanding of large language models
• Optimise and scale our training infrastructure to improve efficiency and reliability
• Develop and improve dev tooling to enhance team productivity
• Contribute to the entire stack, from low-level optimisations to high-level model design

Qualifications & Experience

• Degree (BA required, MS or PhD preferred) in Computer Science, Machine Learning, or a related field
• Strong software engineering skills with a proven track record of building complex systems
• Expertise in Python and deep learning frameworks
• Have worked on high-performance, large-scale ML systems, particularly in the context of language modelling
• Familiarity with ML Accelerators, Kubernetes, and large-scale data processing
• Strong problem-solving skills and a results-oriented mindset
• Excellent communication skills and ability to work in a collaborative environment

You'll thrive in this role if you

• Have significant software engineering experience
• Are able to balance research goals with practical engineering constraints
• Are happy to take on tasks outside your job description to support the team
• Enjoy pair programming and collaborative work
• Are eager to learn more about machine learning research
• Are enthusiastic to work at an organisation that functions as a single, cohesive team pursuing large-scale AI research projects
• Have ambitious goals for AI safety and general progress in the next few years, and you’re excited to create the best outcomes over the long-term

Sample Projects

• Optimising the throughput of novel attention mechanisms
• Proposing Transformer variants, and experimentally comparing their performance
• Preparing large-scale datasets for model consumption
• Scaling distributed training jobs to thousands of accelerators
• Designing fault tolerance strategies for training infrastructure
• Creating interactive visualisations of model internals, such as attention patterns

Benefits

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.

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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You will work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.

Some representative projects include:

• Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters.
• Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.
• Drive performance improvements across our stack through profiling, optimization, and benchmarking.
• Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.

You may be a good fit if you:

• Are proficient in Python and async/concurrent programming with frameworks like Trio.
• Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX).
• Have industry experience in machine learning research.
• Can balance research exploration with engineering implementation.
• Enjoy pair programming (we love to pair!).
• Care about code quality, testing, and performance.
• Have strong systems design and communication skills.
• Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems.

Strong candidates may have:

• Familiarity with LLM architectures and training methodologies.
• Experience with reinforcement learning techniques and environments.
• Experience with virtualization and sandboxed code execution environments.
• Experience with Kubernetes.
• Experience with distributed systems or high-performance computing.
• Experience with Rust and/or C++.

Strong candidates need not have:

• Formal certifications or education credentials.
• Academic research experience or publication history.

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As a Technical Program Manager on the Robotics Operations team, you will apply program management methodologies to multiple research initiatives to accelerate Google DeepMind's Embodied AI goals in a safe and responsible way.

Key responsibilities include partnering closely with data collection teams, research teams, and the Engineering team to establish and govern internal data collection efforts, ensure seamless delivery of new evaluations and data collections, and oversee design and implementation of new environments for data collection or robot platform modifications.

You will also monitor and report on operational efficiency and resource utilization metrics, evaluate and improve processes, tools, templates, and documentation for repeatability and sustainability, and implement processes and methodologies to reduce operational costs.

To succeed in this role, you must thrive in a fast-paced environment and effectively manage cross-functional teams to deliver exceptional value to and accelerate Google DeepMind's research through strategic cross-functional partnerships.

In order to set you up for success as a Technical Program Manager at Google DeepMind, we look for the following skills and experience:

• Experience partnering with Research/Engineering leads on high impact, goal-oriented projects.
• Bachelor's degree in an engineering discipline along with equivalent practical experience of 5+ years.
• Demonstrated ability to improve processes, workflows, and governance models to enhance efficiency within development teams.
• Excellent technical understanding and communication ability, with the ability to distill sophisticated technical ideas to their essence.
• Experience in solving complex challenges, implementing scalable and sustainable solutions.
• Knowledge of, or curious to learn about AI/machine learning research.
• Familiar with hardware and software testing and associated safety protocol.

In addition, the following would be an advantage:

• Robotics domain knowledge and experience.
• Master's degree in a relevant field (Robotics/AI).
• Experience managing operational teams.
• Experience in a technical lab or production environment.

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To succeed in this role, you will need a PhD in a technical field or equivalent practical experience, knowledge of the latest in large machine learning research, and experience working with real-world robots. Expertise in using large datasets with deep neural networks to make real robots useful is also an advantage.

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Our team is focused on ensuring safe humanoid robot actions spanning agentic reasoning, HRI scenarios, and physical safety with VLA models. As a Research Scientist, you will design, implement, train, and evaluate large models and algorithms for humanoid robots. You will make breakthroughs and unlock new humanoid safety capabilities, including algorithmic and model development to improve a robot agent's understanding of its own embodiment and VLA capabilities.

You will write software to implement research ideas and iterate quickly. You will leverage your expertise to participate in a wide variety of research, including learning from simulation, reinforcement learning, learning from demonstrations, vision-language-action models, transformers, video generation, robot control, humanoid robots, and more.

You will work effectively with a large collaborative team with fast-paced agendas to meet ambitious research goals. You will generate creative ideas, set up experiments, and test hypotheses. You will report and present research findings clearly and efficiently both internally and externally.

To be successful as a Research Scientist at Google DeepMind, we look for PhDs in technical fields or equivalent practical experience. You should have knowledge of the latest in large machine learning research and experience working with real-world robots. Expertise with a subset of the following topics would be an advantage: Humanoid Whole Body Control, Vision Language Action models, Motion Planning, Force Control, AI Safety, Diffusion Policies, World Models, Imitation Learning, and Reinforcement Learning.

The US base salary range for this full-time position is between $141,000 - $202,000 + bonus + equity + benefits.

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Research Scientist, Robotics

Role Details

We are looking for Research Scientists to join the Robotics team at Google DeepMind. The team's mission is to build 'Embodied AI' - a robot brain capable of whole-body, dexterous, general and useful physical actions - to improve the lives of billions of people in the physical world.

Key Responsibilities

• Design, implement, train and evaluate large models and algorithms for robotic agents.
• Write software to implement research ideas and iterate quickly.
• Leverage expertise to participate in a wide variety of research, including learning from simulation, reinforcement learning, learning from demonstrations, vision-language-action models, transformers, video generation, robot control, humanoid robots and more.
• Work effectively with a large collaborative team with fast-paced agendas to meet ambitious research goals.
• Generate creative ideas, set up experiments and test hypotheses. Report and present research findings clearly and efficiently both internally and externally.

Requirements

• PhD in a technical field or equivalent practical experience.
• Knowledge of the latest in large machine learning research.
• Experience working with simulators and real-world robots.
• Expertise in using large datasets with deep neural networks to make real robots useful.
• A real passion for AI impacting real world robots!

Benefits

The US base salary range for this full-time position is between $166,000 - $244,000 + bonus + equity + benefits.

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As a TPU Kernel Engineer, you'll be responsible for identifying and addressing performance issues across many different ML systems, including research, training, and inference. A significant portion of this work will involve designing and optimising kernels for the TPU. You will also provide feedback to researchers about how model changes impact performance.

You may be a good fit if you:

• Have significant experience optimising ML systems for TPUs, GPUs, or other accelerators
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning research
• Care about the societal impacts of your work

Strong candidates may also have experience with:

• High performance, large-scale ML systems
• Designing and implementing kernels for TPUs or other ML accelerators
• Understanding accelerators at a deep level, e.g. a background in computer architecture
• ML framework internals
• Language modeling with transformers

Representative projects:

• Implement low-latency, high-throughput sampling for large language models
• Adapt existing models for low-precision inference
• Build quantitative models of system performance
• Design and implement custom collective communication algorithms
• Debug kernel performance at the assembly level

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
• 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.

Guidance on Candidates' AI Usage:

Learn about our policy for using AI in our application process

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

You may be a good fit if you:

• Have significant software engineering experience
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning research
• Care about the societal impacts of your work

Strong candidates may also have experience with:

• High performance, large-scale ML systems
• GPUs, Kubernetes, Pytorch, or OS internals
• Language modeling with transformers
• Reinforcement learning
• Large-scale ETL

Representative projects:

• Optimizing the throughput of a new attention mechanism
• Comparing the compute efficiency of two Transformer variants
• Making a Wikipedia dataset in a format models can easily consume
• Scaling a distributed training job to thousands of GPUs
• Writing a design doc for fault tolerance strategies
• Creating an interactive visualization of attention between tokens in a language model

Annual compensation range for this role is listed below.

Annual Salary:

$350,000 - $500,000USD

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. 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.

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 California, USA.

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Researcher, Synthetic RL

Location

San Francisco

Employment Type

Full time

Department

Research

Compensation

• $295K – $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 Synthetic RL team develops reinforcement learning methods that leverage synthetic data, environments, and feedback to train and evaluate frontier AI models. The team explores approaches such as self-play, simulators, and other synthetic evaluations to push model capability, generalization, and alignment beyond what is possible with the current prevailing methodology.

About the Role

As a Research Scientist on the Synthetic RL team, you will develop novel reinforcement learning techniques that use synthetic environments and feedback to improve large-scale models. You’ll work closely with other researchers to design experiments, analyze learning dynamics, and translate research insights into training approaches used in production systems.

We’re looking for researchers who enjoy working on open-ended problems, value fast iteration, and want their work to directly shape how frontier models are trained.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Research and develop reinforcement learning algorithms

• Design and run experiments to study training dynamics and model behavior at scale

• Collaborate with engineers and researchers to integrate successful approaches into model training pipelines

You might thrive in this role if you:

• Have a strong background in reinforcement learning, machine learning research, or related fields

• Have strong engineering and statistical analysis skills

• Enjoy exploring new problem spaces where data, objectives, and evaluation are imperfect or evolving

• Are motivated by seeing research ideas influence real-world AI systems

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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