RL Systems Engineer for Scalable ML Research

Anthropic Limited; New York (NY); Hybrid; USD 500,000 - 850,000; Full time

Benefits offered by this job

Equity donation matching

Generous vacation and parental leave

Flexible working hours

Office space

Job summary

Anthropic Limited in New York seeks a Research Engineer for the RL Velocity team to build and improve the core RL training infrastructure, remove bottlenecks, and help researchers iterate faster.

You will collaborate with researchers and adjacent engineering teams to ship tooling that makes research progress faster, own the reliability and performance of runs end-to-end, and contribute to design decisions that scale RL at Anthropic.

Qualifications

• 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 leverage platforms rather than individual experiments.
• Comfortable operating across the stack, from low-level performance work to RL algorithms.
• Bias toward shipping and iterating quickly, with high agency and low ego.

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 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

Platform development

Education

Bachelor’s degree

Tools

JAX

PyTorch

Job description

Anthropic Limited in New York seeks a Research Engineer for the RL Velocity team to build and improve the core RL training infrastructure, remove bottlenecks, and help researchers iterate faster.

You will collaborate with researchers and adjacent engineering teams to ship tooling that makes research progress faster, own the reliability and performance of runs end-to-end, and contribute to design decisions that scale RL at Anthropic.