Seed-stage AI for Science Lab | London (on-site)
Overview
RL has pushed coding and maths forward quickly. Biology is harder because evidence is incomplete, feedback is slow, and a model can look like it's improving while quietly gaming the reward. This role exists to build training setups where that can't happen.
The lab is a small, seed-funded founding team in London. They train frontier models as agents on real scientific work: forming hypotheses, planning experiments, reading results.
You'd be one of the first technical hires. There's no inherited stack, so you'd help set the tooling and research standards.
What you'll do
- Train agents with RL on multi-step problems across biology, drug discovery and clinical research
- Build environments where agents query biological data, call scientific tools and act on the output
- Design reward functions and evals that separate real progress from reward hacking
- Build rollout, distributed training and eval infrastructure so a few people can run large experiments
- Own experiments end-to‑end, from design through debugging to reproducible results
- Work with in-house scientists to turn open biological questions into tasks with checkable outcomes
What you'll need
- Hands‑on experience training models or agents with RL
- A first‑principles grasp of reward design, credit assignment, exploration and training stability
- Strong engineering - you write reliable, fast code and can debug from learning dynamics down to GPU performance
- Sound experimental judgement: you know why a run failed and what to try next
- Comfort owning ambiguous problems in a team of a handful of people
- Willingness to work five days a week in the London office
Optional Bonus
- Agentic systems work: tool use, multi-step reasoning or long‑horizon tasks
- Biology or healthcare background - useful, not expected
- PhD or publications in RL, post‑training or scientific ML
Seniority is open, from strong early‑career engineers to senior researchers.
Shortlisted candidates will be contacted within 48 hours.
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