Own ML projects end-to-end, build agentic LLM systems, evaluate models, improve model quality, work across the full AI stack, partner with Product, Clinical, and Engineering, help the team get better.
- 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
- Nice to have: fine-tuning
- Nice to have: RLHF
- Nice to have: DPO
- Nice to have: healthcare AI
- Nice to have: agent frameworks
