Research Engineer, Machine Learning (RL Velocity)

Anthropic•San Francisco, CA

12h•Hybrid

About The Position

The RL Velocity team owns the efficiency and reliability of Anthropic's RL Science stack, which includes the infrastructure, tooling, and systems that enable researchers to iterate quickly on training runs. As a Research Engineer on this team, you will be responsible for building and improving the core platform that supports Anthropic's RL operations, aiming to remove bottlenecks that hinder research and accelerate the delivery of better models across the organization. This role is high-leverage, as even small improvements in velocity can significantly impact every researcher and every training run.

Requirements

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

Nice To Haves

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

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