Senior ML Engineer — AI Care, Production‑Driven

Sword Health; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

Sword Health is hiring for a research-driven ML Engineer to own end-to-end AI projects in production, from exploration to deployment and iterations with real users.

You will build agentic LLM systems, design multi-step workflows, and partner with Product, Clinical, and Engineering to translate clinical requirements into technical decisions while advancing evaluation, safety, and reliability at scale.

Qualifications

• 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: built eval datasets and frameworks and can distinguish signal from noise.
• Strong ML fundamentals: reason about tradeoffs and pick suitable approaches.
• Comfort with ambiguity: turn loosely defined problems into production-ready solutions.
• Solid engineering skills: production-quality code, distributed systems, and debugging ML pipelines.
• Open source contributions or technical writing demonstrate knowledge sharing.

Responsibilities

• Own ML projects end-to-end from exploration to production deployment.
• Build agentic LLM systems with multi-step workflows and tool use.
• Design eval sets, offline/online harnesses, LLM-as-judge pipelines.
• Improve model quality with prompting, retrieval, distillation, or fine-tuning.
• Work across data prep, model adaptation, serving, and monitoring.
• Partner with Product, Clinical, and Engineering to translate requirements into technical decisions.
• Mentor engineers and share learnings across the team.

Skills

End-to-end ML in prod

LLM workflows

Evaluation frameworks

Strong ML fundamentals

Clear communication

Open source contributions

Job description

Sword Health is hiring for a research-driven ML Engineer to own end-to-end AI projects in production, from exploration to deployment and iterations with real users.

You will build agentic LLM systems, design multi-step workflows, and partner with Product, Clinical, and Engineering to translate clinical requirements into technical decisions while advancing evaluation, safety, and reliability at scale.