RL Research Engineer for Science & Drug Discovery (London)

Axiōma Search; Greater London; Presencial; GBP 90.000 - 130.000; Jornada completa

Descripción de la vacante

Seed-stage AI for Science Lab in London (on-site) is seeking an engineer to shape training setups for RL-driven agents in biology and drug discovery. You will design experiments, build environments, and work with scientists to turn open biological questions into testable tasks.

You will contribute to end-to-end experimentation, debugging, and reproducible results, while helping set tooling and research standards in a small, ambitious team.

Formación

• 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

Responsabilidades

• 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

Descripción del empleo

Seed-stage AI for Science Lab in London (on-site) is seeking an engineer to shape training setups for RL-driven agents in biology and drug discovery. You will design experiments, build environments, and work with scientists to turn open biological questions into testable tasks.

You will contribute to end-to-end experimentation, debugging, and reproducible results, while helping set tooling and research standards in a small, ambitious team.