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Posted Mar 17Staff ML Engineer, Frontier AI
San Francisco, United StatesHybrid
Responsibilities
- Identify failure modes, form hypotheses, and drive architecture decisions on hard clinical AI problems — medical coding, adaptive scribing, chart understanding, and more. - Build compounding learning loops.
- Design systems that turn real-world signals — clinician edits, coder corrections, audit outcomes — into fast, safe model improvements. - Improve Chart Chat quality.
- Drive better grounding, smarter retrieval, and reasoning that holds up under the real diversity of clinical questions over complex longitudinal patient records. - Push latency, accuracy, and cost simultaneously.
- Distill insights from recent research — particularly in RL, deep learning, and clinical NLP — and drive experiments that keep Ambience at the frontier of clinical AI.
Requirements
- We’re building the AI intelligence platform that restores humanity to healthcare and drives meaningful ROI for health systems across the country.
- Experience in the KLAS Research Emerging Solutions Top 20 Report, recognized by Fast Company as one of the Next Big Things in Tech, named one of the best AI companies in healthcare by Inc., and selected as a LinkedIn Top Startup in 2024 and 2025.
- THE ROLE: As a Staff ML Engineer on the Frontier AI team at Ambience, you'll own the hardest model quality problems across our clinical AI products — foundational coding models, adaptive scribing, voice agents, long-context chart understanding, and clinical reasoning.
- Ambience ships advanced clinical AI in real-world healthcare settings.
- The models that power our products operate under constraints you won't find in typical ML roles — proprietary ontologies, messy EHR data, high compliance stakes, and clinician workflows where latency and accuracy both matter.
- Experience with preference learning, RLHF, retrieval-augmented generation, or multi-label classification. - Strong Python fundamentals and
- experience with deep learning frameworks (PyTorch preferred).
- Experience with clinical data: EHR systems, FHIR, medical coding ontologies, or clinical NLP. - Prior work in healthcare AI or other regulated, high-stakes domains. - Open-source contributions to ML libraries, benchmarks, or evaluation frameworks.
- You will own the hardest model quality problems across our clinical AI suite: coding models that navigate a proprietary million-term ontology with multi-objective precision, a scribe that learns from edit signals without introducing regressions, long-context chart understanding that stays faithful under real clinical complexity, and population-level reasoning that surfaces patterns across patients in a way that's auditable and actionable.