Agentic AI Engineer (life sciences)

Key Responsibilities
Build and refine AI-agentic workflows on our internal framework to help drug discovery scientists generate the best testable therapeutic hypotheses to progress. Translate discovery problems into agent designs, working out where an agentic approach genuinely adds value, and where it doesn’t Integrate agents with our knowledge graph, proprietary methods, scientific literature and other sources so they reason over the right evidence for each problem Expand and maintain our existing GenAI tools so they keep pace with the team’s needs and a fast-moving ecosystem Contribute to how we evaluate agentic workflows, helping shape sensible evals, testing and quality standards as the practice matures Write clear, maintainable, well-documented code that others on the team can build on

What success looks like
In 3 months, you have: Got to grips with our agentic framework and the discovery problems it serves, and made your first contributions land in the codebase Built working relationships with the scientists and engineers you partner with, and started turning their feedback into concrete improvements In 6 months you have: Taken at least one agentic workflow from idea to a production tool scientists use in our drug discovery pipeline — built on our framework, with sensible evaluation and documentation Operating with real independence — owning agentic workflows end to end, proposing improvements.

What we are looking for
We'd love to hear from you if You've built and shipped LLM-agentic systems that deliver real value — agents that use tools, orchestrate multi-step workflows and behave reliably in production. You have at least 2 years of software engineer or ML engineer working experience. And you have strong software engineering fundamentals — you write clear, tested, maintainable Python code that others can build on Fluency with the modern LLM/agent toolkit — model APIs, prompting, tool use, RAG, and the patterns this fast-moving ecosystem is converging on (MCP, agent frameworks, evals). You enjoy working closely with non-engineers — you are the kind of person who'll sit with a scientist to understand what they actually need. It’s a bonus if you have: Experience in drug discovery, biology, or another life science domain Familiarity with knowledge graphs or reasoning over structured or heterogeneous data Experience building evaluation harnesses or testing strategies for LLM systems A track record of picking up unfamiliar domains quickly and becoming useful fast Interest in or experience with biotech / techbio and its impact on patient outcomes.