Staff ML Engineer, Frontier AI

Here at Ambience, we never set out to be just another scribe. We’re building the AI intelligence platform that restores humanity to healthcare and drives meaningful ROI for health systems across the country.

Our technology helps providers focus on delivering great care by removing the administrative burden that pulls them away from patients and away from their most impactful work. Ambience delivers real-time coding-aware documentation and clinical workflow support across ambulatory, emergency and inpatient settings at the top health systems in North America.

Our teams operate relentlessly with extreme ownership to build the best solutions for our health system partners. We value candor, positivity and deep thought — and we expect a lot from each other because we know the problems we’re solving truly matter.

Ambience was ranked #1 for Improving the Clinician 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. We’re backed by Oak HC/FT, Andreessen Horowitz (a16z), OpenAI Startup Fund, and Kleiner Perkins — and we’re just getting started.

As a Staff Machine Learning Engineer at Ambience, you will help set the technical direction for the AI systems that power our clinical products. You’ll identify the highest-impact opportunities to improve model behavior, evaluation, post-training, and agentic systems, and lead the design and execution of cross-cutting initiatives.
This is a highly hands-on role with broad technical influence. You’ll work closely with clinicians, product managers, researchers, and engineers to translate cutting-edge research into reliable, production-grade AI systems.
Our engineering roles are hybrid — working onsite at our San Francisco office three days per week.

Our products power specialty-specific note generation, chart-aware diagnosis prediction, and real-time clinical decision support in real clinical settings. The work is deeply technical, but the goal is simple: help clinicians do great work with less friction.
To keep improving, we can't wait around for bigger models. The fastest path is building intelligence that gets better with every encounter — learning from how clinicians actually use the products, what they change, and what outcomes follow.
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.
This is not applied ML on clean benchmarks — it's research-grade model work with production stakes, where your improvements directly shape what clinicians experience every day.