Research Engineer, ML Infrastructure for RL Systems

Anthropic Limited; Greater London; Hybrid; GBP 110,000 - 170,000; Full time

Job summary

Anthropic is seeking a Research Engineer, Machine Learning (RL Velocity) in London to own and improve the RL training infrastructure used by researchers day-to-day. You will work on core platform components that speed up experiments, reduce runtime variability, and help scale RL workflows across teams.

Collaborating with researchers and adjacent engineering groups (inference, sandboxing, and tooling), you will diagnose bottlenecks, implement robust solutions, and shape how Anthropic does RL at

Qualifications

• Bachelor’s degree or equivalent in a relevant field.
• Experience building ML infrastructure or research tooling.
• Strong software engineering fundamentals and performance mindset.

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.

Skills

Software engineering

ML infrastructure

Distributed systems

Research tooling

Shipping mindset

Cross-functional collaboration

Education

Bachelor’s degree

Tools

JAX

PyTorch

Job description

Anthropic is seeking a Research Engineer, Machine Learning (RL Velocity) in London to own and improve the RL training infrastructure used by researchers day-to-day. You will work on core platform components that speed up experiments, reduce runtime variability, and help scale RL workflows across teams.

Collaborating with researchers and adjacent engineering groups (inference, sandboxing, and tooling), you will diagnose bottlenecks, implement robust solutions, and shape how Anthropic does RL at