Senior Research Engineer - AI for Biology
Build and optimize distributed training systems for frontier-scale foundation models trained on proprietary biological datasets. Own GPU optimization, experiment frameworks, and tooling that enables research teams to push hardware limits. Requires full-stack ML engineering expertise and strong software engineering habits.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Job Description
Senior Research Engineer | AI for Biology
Central London · On-site · £130,000 to £230,000 base + equity
Relocation support provided, including Visa Sponsorship for candidates working at a frontier lab offices based out of the UK.
Build the training systems behind foundation models trained on data no other lab has.
I'm working with a London AI research company that trains large foundation models for the life sciences. They own a biological dataset that is bigger and more diverse than anything public. They collected it themselves from environments most datasets never touch. Big Tech labs cannot copy their models by throwing compute at public data, because the data itself is the moat that took years to secure. This is truly a herculean task.
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Compute is not a bottleneck. The research team runs like a frontier lab rather than a typical biotech and we're looking to add multiple hires.
The role
You will own the systems that power their research: distributed training pipelines, GPU optimisation, experiment frameworks, and the tooling that lets a team work at frontier scale. You sit inside the research team and report to the Head of AI Research. Your decisions shape what experiments are possible and how fast ideas move from whiteboard to result. Custom architectures on new data types. Training runs that push hardware limits. A pace of experimentation that demands robust, flexible tooling.
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What they want
- Industry experience at an AI lab where research engineers are key contributors. They have been clear that this search favours people from industry over purely academic backgrounds.
- Full-stack depth: distributed training frameworks such as Megatron-LM, DeepSpeed or FSDP, GPU performance work, and experience down to the CUDA, Triton or XLA level.
- Contributions to open-source ML frameworks or research codebases. They genuinely value this.
- Strong software engineering habits. Clean code, good tests, a focus on performance, and systems other people can build on.
- Real curiosity about biology. You do not need bio experience, but the prospect should excite you.
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Pay and process
£130k to £230k base depending on experience, plus meaningful equity. They will stretch above the top of the band for an exceptional candidate from a frontier lab. The interview process is rigorous so before the technicals candidates will have a more informal chat with the Head of Research before fully commiting. Expect a technical round on ML fundamentals, hands-on coding, and end-to-end systems design.
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