The opportunity
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
What you'll do
Partner deeply with flagship life sciences research institutions: understand their scientific workflows end-to-end, build hands-on with their engineering teams, and help take projects from early exploration to production systems integrated into how they do science day-to-day.
Develop reusable ecosystem infrastructure, like MCP servers for: domain-specific data sources (genomics platforms, literature databases, experimental repositories), instruments, scientifically-grounded benchmarks, and agent skills that other institutions can adopt without starting from scratch.
Identify what's actually hard about deploying AI in life sciences: (heterogeneous data, auditability requirements, the prototype-to-trust gap) and feed those findings back to product, engineering, and research.
Create technical content and documentation that lets partners self-serve, so: what works for one institution can scale globally without the same level of hand-holding.
Deep research experience in life sciences, biomedical research, or scientific: computing. Bonus if you've studied genomics, neuroscience, or drug discovery specifically and are comfortable getting deeply technical with academics.
Experience building LLM-powered tools or applications: prompting, context engineering, agent architectures, evaluation frameworks.
What they're looking for
- Builder credibility from shipping production code as a software engineer,: forward-deployed engineer, or technical founder.
- A scrappy mentality–comfortable wearing multiple hats, building from scratch,: driving clarity in ambiguous situations, and doing whatever it takes to further the mission.
