Type: Full-time | Remote (US) — San Francisco preferred
Compensation: $150K – $250K + equity
Visa sponsorship: Not available (TN available; no H-1B)
About The Company
A Series B healthcare AI company building AI agents to automate and transform clinical workflows — from ambient clinical documentation to billing automation and clinical trial matching. The company's AI ambiently captures patient visits and writes complete, billable documentation directly within the clinician's electronic health record, helping reduce clinician burnout and improve patient care. Backed by top-tier investors with $60M+ raised, and trusted by some of the largest healthcare organizations in the country.
Founded: 2017 | Team size: :60 | Industry: Healthcare, AI | Location: San Francisco, CA (HQ); remote within the US
The Role
We're looking for an AI Engineer to build and ship LLM-powered healthcare applications, with ownership over new AI workflows from prototype to production. This is a deeply technical role that blends ML, product thinking, and engineering — you'll build and own production services that rely on LLMs, speech models, and medical reasoning. Expect to ship fast, talk directly to customers, and build systems that genuinely reduce clinician burnout.
What You'll Do
- Develop and own end-to-end AI applications such as clinical trial matching, ambient copilots, and billing automation.
- Improve an LLM-powered documentation platform used daily by thousands of clinicians.
- Build and optimize inference pipelines and real-time speech recognition systems.
- Collaborate closely with PMs, designers, clinical experts, and GTM teams to iterate rapidly and launch high-impact features.
Tech stack: Python, TypeScript, LLM tooling (LangGraph, Mastra, Agents SDK), LLM evals + applied GenAI
What We're Looking For
Required
- 2–6 years of experience as a software engineer, with work on AI and LLM-focused products
- Experience building complex AI applications end-to-end (LLM inference work, agent products, etc.)
- A STEM undergraduate degree
- Ability to work across the stack (frontend, backend, infra) to problem-solve and ship features
- Applied-AI literacy — comfortable reasoning about evals, statistics, and the non-determinism of LLM systems
- Obsessed with speed, ownership, and getting real user feedback
Nice to Have
- Experience in a fast-moving startup or as a founder
- Experience with ASR systems (e.g., Whisper)
- Familiarity with SOC-2, HIPAA, or sensitive data pipelines
- Experience with EHR integrations (FHIR, HL7) or healthcare-specific ontologies
Interview Process
- Engineering Manager screen (30 min) — your experience, projects, how you operate, and how you use AI day-to-day.
- Coding & problem-solving interview (1 hr) — design and code a word-based game problem; bring your own IDE.
- AI challenge interview (1 hr) — build a bot that solves a word-based game; discuss trade-offs and evaluation.
- Product team conversation (30 min) — cultural fit, problem-solving, and how you handle adversity.
- Senior/staff deep dive (45 min) — deeper AI design/research discussion for senior+ candidates.
- Offer.
