Staff ML Engineer, Frontier AI
Staff ML Engineer owns foundational model research and end-to-end quality improvements for clinical AI products like coding models, adaptive scribing, and chart understanding. Requires 5+ years ML experience with deep RL/deep learning expertise and production shipping track record.
About the job
What You’ll Own
- Own foundational model research. 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. Apply the right optimization levers — capability routing, distillation, speculative decoding, quantization — and know when each is safe.
- Contribute to population-level clinical reasoning. Help build toward a layer of clinical intelligence that reasons not just over individual patients, but across patient populations at scale.
- Stay at the cutting edge. 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.
Who You Are
Deep RL and Deep Learning Expertise
- 5+ years of ML engineering or applied research experience, with a strong track record of shipping model improvements in production.
- Deep expertise in reinforcement learning and deep learning, developed in industry or a research setting.
- Publications at top-tier venues (NeurIPS, ICML, ICLR, ACL, EMNLP, etc.) are a strong plus.
Research to Production
- Comfortable spanning research and engineering — architecture decisions, training runs, fine-tuning pipelines, and production deployment.
- Experience with preference learning, RLHF, retrieval-augmented generation, or multi-label classification.
- Strong Python fundamentals and experience with deep learning frameworks (PyTorch preferred).
End-to-End Ownership
- Can point to model quality improvements driven end to end: from identifying a failure mode to shipping and measuring a fix.
- Has operated at the frontier of a hard problem, not just applied standard techniques.
- Staff-level scope — has owned research directions and influenced technical decisions across teams.
Mission-Aligned
- Passion for healthcare or other high-stakes, mission-driven industries.
- Thrives in a fast-paced environment; takes extreme ownership of deliverables.
Nice-to-haves
- 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.
Compensation
Base compensation range of approximately $250,000-350,000 per year, exclusive of equity. Flexible cash and equity mix based on preferences. Comprehensive medical, dental, vision; 401(k) with 3% match; parental leave.
Skills
PyTorch, Python, Reinforcement Learning, Deep Learning, RLHF, Retrieval-Augmented Generation, Fine-Tuning, Clinical Nlp, Ehr, FHIR
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