Key responsibilities include:

• Building and refining agentic workflows that help scientists generate the best testable hypotheses to progress.
• Translating discovery problems into agent designs, and being clear-eyed about where an agentic approach adds value and where it does not.
• Integrating agents with internal knowledge sources so they reason over the right evidence for each problem.
• Expanding and maintaining existing GenAI tooling as the team's needs and the wider ecosystem evolve.

What we are looking for:

• You have shipped LLM-agentic systems that deliver real value in production, not just prototypes, with tools, multi-step orchestration and reliable behaviour.
• You have at least two years of software engineering or ML engineering experience, with strong fundamentals and clean, tested, maintainable Python.
• You are fluent in the modern LLM and agent toolkit, including model APIs, prompting, tool use, RAG, MCP, agent frameworks and evals.
• You enjoy working closely with non-engineers and are comfortable sitting with a scientist to understand what they actually need.

Experience in drug discovery, biology or another life science domain is a bonus, as is familiarity with knowledge graphs, experience building evaluation harnesses for LLM systems, or a track record of picking up unfamiliar domains quickly.