60K–85K EUR / year
Senior ML Engineer
Machine learningWorkplace — unknown
Published on 2026-09-09
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About this role
Own machine learning projects from exploration through production and continually improve them using real-world feedback. Build and evaluate reliable agentic LLM systems for clinical care, collaborating with Product, Clinical, and Engineering stakeholders.
About the company
Sword Health is a digital musculoskeletal care company that helps employers and health plans deliver AI-powered physical therapy and related care programs.
What you'll do
- Own ML projects end-to-end, from exploration and production deployment to ongoing iteration
- Build agentic LLM workflows using tools, retrieval, and orchestration
- Develop evaluation datasets, offline and online testing, LLM-as-judge workflows, human review, and regression tests
- Improve model quality using evidence-based prompting, retrieval, distillation, or fine-tuning
- Work across data preparation, model adaptation, serving, monitoring, and production feedback loops
- Partner with Product, Clinical, and Engineering to translate clinical needs into technical decisions
- Review code, share knowledge, and mentor less experienced engineers
What we're looking for
- Experience shipping production ML systems that people depend on
- Production LLM experience, including prompting, retrieval, tool calling, and agent-style workflows
- Experience building evaluation datasets and frameworks and distinguishing meaningful improvements from noise
- Strong ML fundamentals and ability to reason about technical tradeoffs
- Ability to turn ambiguous problems into production systems
- Production-quality engineering skills, familiarity with distributed systems, and experience debugging ML pipelines
- Clear communication with technical and clinical stakeholders
Nice to have
- Fine-tuning or preference optimization experience, such as RLHF or DPO
- Healthcare AI or experience in other high-stakes domains
- Experience building agent frameworks or evaluation tools from scratch
- Open-source contributions, technical writing, or other knowledge sharing