Workplace
On-site
Employment
Full-Time
Published
Aug 11, 2026
Closes
No date supplied
The role
About Us: 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. THE ROLE: As a Senior Machine Learning Engineer at Ambience, you will build and improve the AI systems that power our clinical products. You’ll own complex projects end-to-end, from diagnosing production failures and designing evaluations to building, deploying, and iterating on model and agentic systems. This is a highly hands-on role with significant technical ownership. You’ll work closely with clinicians, product managers, and fellow 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. WHAT YOU’LL DO: - Build Trustworthy AI Evaluation Systems: Design and own evaluation pipelines for LLM and agenti
Requirements
Department: Engineering; Team: Engineering