Joblaze summary

In this role, the Research Engineer will focus on enhancing the RL training infrastructure that supports researchers at Anthropic, streamlining processes to boost efficiency and reliability. Key skills include strong software engineering fundamentals and experience with machine learning infrastructure, particularly in distributed systems. This position is ideal for candidates with a background in ML tooling who thrive in collaborative environments and are eager to facilitate the work of others. Anthropic emphasizes a cohesive team approach, aiming for impactful AI research through shared large-scale efforts.

Joblaze insights

Listed about 6 months ago — first seen on Joblaze April 24, 2026. Last confirmed on Anthropic's careers page October 31, 2026.
This exact title is also open at 1 other location at Anthropic: Remote-Friendly (Travel-Required) | San Francisco, CA | New York City, NY.
Starts above 97% of 234 comparable mid ai/ml roles we track (median $150,000 across 81 companies).
AI/ML appears in 41.2% of 51 comparable mid ai/ml roles in United Kingdom; JAX appears in 13.7% of 51 comparable mid ai/ml roles in United Kingdom.

Quick facts

Is the Research Engineer, Machine Learning (RL Velocity) role remote?: It's hybrid — Anthropic expects some on-site time in London, UK.
What's the salary range?: Anthropic lists £370,000–£630,000 for this role.
Where is the role based?: Anthropic is hiring for this position in London, UK.
What's the tech stack?: Joblaze extracted these technologies from the posting: AI/ML, JAX, PyTorch.
Does Anthropic sponsor work visas for this role?: Yes — the posting indicates visa sponsorship is available for the right candidate.
What seniority level is this role?: Anthropic targets mid-level candidates for this position.
Is this full-time or contract?: Full-time for this Research Engineer, Machine Learning (RL Velocity) role at Anthropic.

From the original posting

About Anthropic

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.

Annual Salary:

£370,000—£630,000 GBP

Logistics

Standard company text repeated across Anthropic's postings is omitted here.

Research Engineer, Machine Learning (RL Velocity)