Research Engineer (Machine Learning, Reinforcement Learning Velocity)

Anthropic; York and North Yorkshire; On-site; GBP 90,000 - 150,000; Full time

Benefits offered by this job

Health Insurance

Fertility Benefits

Parental Leave

Flexible PTO

Mental Health

Salary & Equity

Equity Matching

Retirement Plans

Life & Income Protection

Wellness Stipend

Commuter Benefits

Education Stipend

Home Office Stipend

Relocation Support

Meal Service

Job summary

Anthropic’s RL Velocity team is seeking a Research Engineer to build and improve the RL training infrastructure researchers rely on daily, removing bottlenecks and enabling rapid iteration across experiments.

You will own the reliability and performance of research runs end-to-end, collaborating with researchers and adjacent engineering teams to ship scalable tooling that accelerates progress in RL at scale.

Qualifications

• Have worked on ML infrastructure, distributed systems, or research tooling.
• Care about enabling other people’s work and find leverage through platforms rather than individual experiments.
• Bias toward shipping and iterating quickly, with a mix of high agency and low ego.
• Comfortable across the stack, from low-level performance work to RL algorithms.
• Strong software engineering fundamentals and a track record of building performant, reliable systems.
• Experience with large-scale distributed training (RL, pre-training, or post-training).
• Familiarity with JAX, PyTorch, or similar ML frameworks.

Responsibilities

• Build and improve the RL training infrastructure that researchers depend on day-to-day.
• Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed.
• Partner closely with researchers and with adjacent engineering teams to understand pain points and ship tooling that makes them faster.
• Own the reliability and performance of research runs end-to-end.
• Contribute to design decisions that shape how Anthropic does RL at scale.

Skills

Python

Distributed systems

Performance optimization

Software engineering fundamentals

RL algorithms

Education

Bachelor’s degree or equivalent

Tools

PyTorch

JAX

TensorFlow

Job description

• The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs
• As a Research Engineer on the team, you’ll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster
• This is high-leverage work: small improvements to velocity compound across every researcher and every run
• Build and improve the RL training infrastructure that researchers depend on day-to-day
• Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed
• Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster
• Own the reliability and performance of research runs end-to-end
• Contribute to design decisions that shape how Anthropic does RL at scale

Benefits

• Comprehensive health, dental, and vision insurance for you and your dependents
• Inclusive fertility benefits via Carrot Fertility
• 22 weeks of paid parental leave
• Flexible paid time off and absence policies
• Mental health support for you and your dependents
• Competitive salary and equity packages
• Optional equity donation matching at a 1:1 ratio, up to 25% of your equity grant
• Retirement plans with competitive matching
• Life and income protection plans
• $500/month flexible wellness and time saver stipend
• Commuter benefits
• Annual education stipend
• Home office stipends
• Relocation support for those moving for Anthropic
• Daily meals and snacks in the office

Have worked on ML infrastructure, distributed systems, or research toolingCare about enabling other people’s work and find leverage through platforms rather than individual experimentsHave a bias toward shipping and iterating quickly, with a mix of high agency and low egoAre comfortable operating across the stack, from low-level performance work to RL algorithmsHave strong software engineering fundamentals and a track record of building performant, reliable systemsExperience with large-scale distributed training (RL, pre-training, or post-training)Familiarity with JAX, PyTorch, or similar ML frameworksA track record of operating at the edge of research and infra in a fast-moving environmentRequired field of study: A field relevant to the role as demonstrated through coursework, training, or professional experienceMinimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experienceMinimum years of experience: Years of experience required will correlate with the internal job level requirements for the positionWe 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 listedResearch 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