Senior ML Engineer

At Sword, we’re building AI to heal billions and unlock humanity’s full potential. In doing so, we’re pioneering AI Care, a fundamentally new approach to healthcare built for medical reasoning, safety, and real-time treatment, not generic technology applied after the fact. As both a clinical-centric frontier AI lab and an applied AI platform, Sword is reimagining how care is delivered at scale, removing traditional barriers like appointments, waiting rooms, and stigma so more people can access the care they need—and ultimately get back to lives lived in full.

Since 2020, Sword has expanded across physical therapy, women’s health, cardiometabolic, and mental health, and is now moving beyond the session to a fully AI-native, 24/7 care program that brings physical activity, therapeutic exercise, psychotherapy, nutrition, and behavior change into one connected experience. More than 700,000 members across three continents have completed over 10 million AI sessions, helping 1,000+ enterprise clients avoid more than $1 billion in unnecessary healthcare costs.

Backed by 42 clinical studies, 44+ patents, and more than $500 million raised from leading investors including Khosla Ventures, General Catalyst, and Founders Fund, Sword is defining a new standard for healthcare.

AI fluency is a core expectation ry candidate is assessed against our three-level framework — be ready to share real examples of how AI is already part of how you work.

Explorer (Level
1) — Uses AI daily to boost personal productivity

Builder (Level
2) — Creates workflows and tools that elevate the whole team

Integrator (Level
3) — Embeds AI into products and processes at scale

Every hire must demonstrate at least Level 1. The expected level will vary depending on the seniority of the role.

• 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;
• Partner with Product, Clinical, and Engineering: translate clinical requirements into technical decisions and surface trade‑offs early;
• Help the team get better: review code, share what you learn, and mentor engineers earlier in their careers.

• 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: you've built eval datasets and frameworks, and you can tell a real improvement from noise;
• Strong ML fundamentals: you know which approach fits which problem and can reason clearly about tradeoffs;
• Comfort with ambiguity: you've taken loosely defined problems and turned them into something running in production;
• Solid engineering skills: production‑quality code, familiarity with distributed systems, and the patience to debug messy ML pipelines;
• Clear communication with both technical and clinical stakeholders;
• Experience with fine‑tuning or preference optimization (RLHF, DPO, or similar);
• Healthcare AI, or other high‑stakes domains where errors carry real cost;
• Built agent frameworks…