Senior ML Engineer

Responsibilities

• 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
• Improve model quality with whatever fits the problem: prompting, retrieval, distillation, or fine-tuning, chosen on evidence rather than habit
• Work across the full AI stack: data prep, model adaptation, serving, monitoring, and the feedback loops that keep systems improving in production
• Partner with Product, Clinical, and Engineering: translate clinical requirements into technical decisions and surface tradeoffs early
• Help the team get better: review code, share what you learn, and mentor engineers earlier in their careers.

Languages