About the role
We're hiring a software engineer to help create the data pipelines, machine learning infrastructure, and agentic systems that power our science. On the agentic side, that means writing the code that runs the loop an AI agent operates inside, not one-off prompts — designing how agents plan, call tools, orchestrate sub-agents, keep memory, check their own work, and decide when they're done, then shipping them into production to do real work across our genetics and drug-discovery pipeline.
What they're looking for
- Build agent loops end-to-end — tool/function calling, planning, sub-agent orchestration, memory, and control flow that runs reliably and unsupervised
- Build the eval and observability harness — the tests, metrics, tracing, and guardrails that make agentic systems trustworthy in production
- Turn scientific and operational workflows into agents that take real action across our platform
- Ship full-stack features: React/Tailwind front ends and backend services that put these agents in front of scientists
- Design and launch production-grade systems, services, data pipelines, and machine learning infrastructure from scratch
- Use frontier AI-native development tools (e.g. Claude Code) to move fast
More about this role
Foresite Labs is a translational R&D team that derives insights from precision measurement and population-scale biology and genetics to address unmet clinical needs. We use human genetics to systematically dissect and understand human disease biology and develop and critically evaluate therapeutic hypotheses. We engage in translational research, transforming basic insights into therapeutic opportunities. Our work supports drug discovery and company formation, and provides the core around which new ideas are realized and incubated. We offer competitive salaries, excellent benefits, a flexible work environment, and the opportunity to learn from top thinkers in various disciplines. Foresite Labs is headquartered in San Francisco.
We're hiring a software engineer to help create the data pipelines, machine learning infrastructure, and agentic systems that power our science. On the agentic side, that means writing the code that runs the loop an AI agent operates inside, not one-off prompts — designing how agents plan, call tools, orchestrate sub-agents, keep memory, check their own work, and decide when they're done, then shipping them into production to do real work across our genetics...
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