Research Engineer, Machine Learning (RL Velocity)

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

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

Are comfortable operating across the stack, from low-level performance work to RL algorithms

Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego

Strong candidates may also have

Experience with large-scale distributed training (RL, pre-training, or post-training)

Familiarity with JAX, PyTorch, or similar ML frameworks

A track record of operating at the edge of research and infra in a fast-moving environment

Deadline to apply: None. Applications will be reviewed on a rolling basis.

The annual compensation range for this role is listed below.

For 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.

Annual Salary: