Senior ML Engineer

What You'll Do:

• Own ML projects end-to-end: take problems from exploration through production deployment and keep iterating once real users are on them.
• Build agentic LLM systems: design multi-step workflows with tool use, retrieval, and orchestration, and make them reliable enough for clinical settings.
• Treat evaluation as core engineering work: build eval sets, offline and online harnesses, LLM-as-judge pipelines with human review, and regression tests that catch quality drops before they reach users.

What You Need:

• Experience shipping ML systems to production that people actually depend on.
• Hands-on LLM work in production: prompting, retrieval, tool calling, and agent-style workflows.
• A rigorous approach to evaluation: you've built eval datasets and frameworks, and you can tell a real improvement from noise.

Why Join:

• Partner with Product, Clinical, and Engineering to translate clinical requirements into technical decisions.
• Help the team get better by reviewing code, sharing learnings, and mentoring earlier-career engineers.
• Work across the full AI stack: data prep, model adaptation, serving, monitoring, and feedback loops for continuous improvement.