Building the infrastructure that turns medical evidence into clinical action.
Head of Applied AI Engineering
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This team is constructing a reasoning layer that ingests sprawling, often contradictory biomedical data—trial results, real-world evidence, genomic profiles, payer formularies—and surfaces precise, auditable decisions for clinicians, researchers, and care navigators. The surface area spans everything from retrieval-augmented generation pipelines over millions of documents to real-time inference systems that sit inside provider workflows, all with the latency, explainability, and correctness demands that healthcare imposes. As the most senior engineering leader on the applied AI side, you will own the architecture that connects large models to production-grade APIs, define how the organization evaluates safety and accuracy in high-stakes domains, and shape a growing team of engineers who ship systems that directly alter patient trajectories.
What they're looking for
- 10+ years building and leading production engineering teams, with deep experience in applied AI or ML systems that serve external users at scale.
- Strong fluency in Python and TypeScript or NodeJS, plus a track record of designing reliable data-intensive services on cloud infrastructure (AWS, Kubernetes, PostgreSQL, Redis, Spark).
- Comfort working in a regulated or evidence-driven domain—healthcare, life sciences, or similarly high-consequence environments where model behavior must be traceable and defensible.
- Hands-on history with LLM orchestration frameworks like LangChain and the judgment to know when a language model is the wrong tool.
- An instinct for leading through technical taste rather than process, especially in an organization where engineering, product, and clinical experts collaborate daily.