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# Research Engineer, Machine Learning (RL Velocity)

- **Company:** Humanloop
- **Location:** London, UK
- **Salary:** £57,000.0 - £73,000.0
- **Contract:** Permanent contract
- **Skills:** Software Debugging, Distributed Systems, Machine Learning, Tensorflow, Pytorch, Machine Learning Operations
- **Published:** August 27, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5856704417

## About the Role

* We expect a Bachelors degree or an equivalent combination of education, training, and/or experience.
 * We look for a field relevant to the role, demonstrated through coursework, training, or professional experience.
 * We expect years of experience to align with the internal job level requirements for the position.
 * You should have strong software engineering fundamentals and a track record of building performant, reliable systems.
 * You should have experience with ML infrastructure, distributed systems, or research tooling.
 * You should care about enabling other peoples work and find leverage through platforms rather than individual experiments.
 * You should be comfortable operating across the stack, from low-level performance work to RL algorithms.
 * You should have a bias toward shipping and iterating quickly, with high agency and low ego.
 * Strong candidates may also have experience with large-scale distributed training, such as RL, pre-training, or post-training.
 * Strong candidates may also have familiarity with JAX, PyTorch, or similar ML frameworks.
 * Strong candidates may also have a track record of operating at the edge of research and infrastructure in a fast-moving environment.

## Description

* We build and improve the RL training infrastructure that researchers depend on day to day.
 * We identify and remove bottlenecks across the RL stack, including debugging, profiling, and rearchitecting where needed.
 * We partner closely with researchers and adjacent engineering teams, including inference, sandboxing, and others, to understand pain points and ship tooling that makes them faster.
 * We own the reliability and performance of research runs end to end.
 * We contribute to design decisions that shape how we do RL at scale.


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