Research Engineer, Machine Learning (RL Velocity)

anthropic; New York, San Francisco (NY, CA); On-site; USD 500,000 - 850,000; Full time

Benefits offered by this job

Competitive compensation

Generous vacation and parental leave

Flexible working hours

Job summary

Anthropic is seeking a Research Engineer for the RL Velocity team located in New York City. The engineer will enhance the RL Science stack, focusing on reliability and performance of research runs. Responsibilities include improving core infrastructure and collaborating with researchers. Ideal candidates have software engineering skills and ML infrastructure experience. The compensation is competitive, ranging from $500,000 to $850,000, with a hybrid work policy allowing for flexible office time.

Qualifications

• Strong track record of building performant, reliable systems.
• Experience with large-scale distributed training.
• Comfortable operating across the stack.

Responsibilities

• Build and improve RL training infrastructure.
• Identify and remove bottlenecks in the RL stack.
• Partner closely with researchers to enhance tooling.

Skills

Software engineering fundamentals

ML infrastructure

Distributed systems

Research tooling

Performance optimization

Education

Bachelor’s degree or equivalent

Tools

JAX

PyTorch

Job description

Research Engineer, Machine Learning (RL Velocity)

Remote-Friendly (Travel-Required) | San Francisco, CA | New York City, NY

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 organization 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 re‑architecting when needed
• Partner closely with researchers and adjacent engineering teams (inference, sandboxing, etc.) 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 infrastructure in a fast‑moving environment

$500,000 – $850,000 USD

Location and Logistics

• Minimum education: Bachelor’s degree or equivalent combination of education, training, and/or experience
• Location-based hybrid policy: Expect all staff to be in one of our offices at least 25% of the time. Some roles may require more office time.
• Visa sponsorship: We sponsor visas when feasible and will make reasonable efforts to secure one for an offer recipient.

Benefits

Competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space to collaborate with colleagues.

Equal Employment Opportunity

As set forth in Anthropic’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.