Staff ML Engineer (Frontier AI)

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

This isn’t a platform or infrastructure role. You’ll set research direction, design learning loops, and drive end-to-end model quality improvements that compound over time

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

You’ll bring deep research instincts and engineering discipline to push the frontier on all of it

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

Experience with preference learning, RLHF, retrieval-augmented generation, or multi-label classification

Comfortable spanning research and engineering — architecture decisions, training runs, fine-tuning pipelines, and production deployment

Passion for healthcare or other high-stakes, mission-driven industries

Publications at top-tier venues (NeurIPS, ICML, ICLR, ACL, EMNLP, etc.) are a strong plus

Thrives in a fast-paced environment; takes extreme ownership of deliverables

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

Has operated at the frontier of a hard problem, not just applied standard techniques

Strong Python fundamentals and experience with deep learning frameworks (PyTorch preferred)

Can point to model quality improvements driven end to end: from identifying a failure mode to shipping and measuring a fix

Staff-level scope — has owned research directions and influenced technical decisions across teams

Open-source contributions to ML libraries, benchmarks, or evaluation frameworks

Experience with clinical data: EHR systems, FHIR, medical coding ontologies, or clinical NLP

Prior work in healthcare AI or other regulated, high-stakes domains