Staff ML Engineer, Frontier AI

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.