About the role
Sword Health is hiring a Senior ML Engineer to own machine learning projects end to end, from early exploration through production deployment and ongoing iteration with real users. The role sits at the intersection of applied AI and clinical care, so reliability and evaluation are treated as first-class engineering concerns rather than afterthoughts.
You will build agentic LLM systems with tool use, retrieval, and orchestration, and make them dependable enough for clinical settings. The team expects you to design eval sets, offline and online harnesses, LLM-as-judge pipelines with human review, and regression tests that catch quality drops before they reach members. You will also improve model quality through prompting, retrieval, distillation, or fine-tuning, choosing approaches based on evidence, and work across data prep, model adaptation, serving, monitoring, and feedback loops.
Highlights
- Own ML projects end to end, including production deployment and iteration
- Build agentic LLM workflows with tool use, retrieval, and orchestration
- Treat evaluation as core engineering work with robust harnesses and regression tests
- Partner with Product, Clinical, and Engineering to translate requirements into technical decisions
This recap is dataskew's editorial summary, not the company's copy.