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Senior ML Engineer
Sword Health. Own ML projects end-to-end, from exploration through production deployment and ongoing iteration .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in end-to-end machine learning project ownership, including model adaptation, evaluation, and deployment in clinical settings. Proficient in building and refining LLM workflows, with strong communication skills to collaborate with technical and clinical teams.
Highest-signal resume keywords
End-To-End ML Project OwnershipLLM Workflow DesignModel Evaluation and DebuggingProduction-Quality Coding SkillsClear Communication with Stakeholders
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine Learning FundamentalsPrompting and RetrievalModel Fine-TuningEvaluation Dataset CreationDebugging ML PipelinesDistributed Systems FamiliarityAgent Framework DevelopmentRegression TestingProduction DeploymentTechnical Decision Making
Soft Skills
MentoringKnowledge SharingClear Communication
Certifications & Qualifications
Level 1 AI Proficiency
Industry Keywords
Healthcare AIHigh-Stakes Domain ExperienceOpen Source ContributionsTechnical Writing
Tech Stack
Tools & technologiesDistributed SystemsOpen Source
About the role
Key responsibilities & impact- Own ML projects end-to-end, from exploration through production deployment and ongoing iteration
- Design multi-step agentic LLM workflows with tool use, retrieval, and orchestration for clinical settings
- Build evaluation datasets, offline and online harnesses, LLM-as-judge pipelines with human review, and regression tests
- Improve model quality through prompting, retrieval, distillation, or fine-tuning based on evidence
- Work across data preparation, model adaptation, serving, monitoring, and production feedback loops
- Partner with Product, Clinical, and Engineering to translate clinical requirements into technical decisions
- Review code, share knowledge, and mentor less experienced engineers
Requirements
What you’ll need- 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, including building eval datasets and frameworks
- Strong ML fundamentals and ability to reason about tradeoffs
- Ability to turn loosely defined problems into production systems
- Production-quality coding skills
- Familiarity with distributed systems
- Ability to debug messy ML pipelines
- Clear communication with technical and clinical stakeholders
- Demonstrate at least Level 1 AI proficiency
- Bonus: Experience with fine-tuning or preference optimization (RLHF, DPO, or similar)
- Bonus: Healthcare AI or other high-stakes domain experience
- Bonus: Experience building agent frameworks or evaluation tooling from scratch
- Bonus: Open source contributions, technical writing, or other knowledge sharing
Benefits
Comp & perks- Sword also offers additional benefits beyond total compensation
- Country-specific benefits may vary and will be detailed separately
- Variable, bonus, or sales incentives may be included in total compensation
- Private company stock options may be included in total compensation