Senior ML Engineer

What you’ll be doing:

• 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.

What you need to have:

• 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;
• Strong ML fundamentals: you know which approach fits which problem and can reason clearly about tradeoffs;
• Comfort with ambiguity: you've taken loosely defined problems and turned them into something running in production;
• Solid engineering skills: production-quality code, familiarity with distributed systems, and the patience to debug messy ML pipelines;
• Clear communication with both technical and clinical stakeholders.

Bonus points:

• Experience with fine-tuning or preference optimization (RLHF, DPO, or similar);
• Healthcare AI, or other high-stakes domains where errors carry real cost;
• Built agent frameworks or evaluation tooling from scratch;
• Open source contributions, technical writing, or other knowledge sharing.