Research Engineer, Machine Learning (RL Velocity)

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

GBP 370K-630K From the posting source

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

U.S. benchmark only; posted salary is not compared across countries or currencies.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source

Seniority

Mid From the posting source

Schedule

Shift type

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Weekend work

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Company

Equity

Offered From the posting source

Application

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Research Engineer, Machine Learning (RL Velocity) London, UK About Anthropic 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. About the role 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. 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 (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 You may be a good fit if you - Have strong software engineering fundamentals and a track record of building performant, reliable systems -

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