Senior Machine Learning Engineer

United States·San Franciscofull-timesenior

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

Build Trustworthy AI Evaluation Systems: Design and own evaluation pipelines for LLM and agentic systems, combining automated graders, regression testing, production feedback,

Technical Tools

Machine Learning EngineerData

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 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.

Responsibilities

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Nice to Have

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Experience with realtime voice, conversational AI, or multimodal systems.

Experience with fine-tuning, post-training, or model adaptation.

Prior work in healthcare, clinical AI, or other regulated, high-stakes industries.

Experience interviewing or mentoring ML engineers.

Open-source contributions to ML, agent, or evaluation tooling.

What We Offer

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Working at Ambience means opting into a high-ownership, high-trust environment built for people who want to grow fast, operate decisively and focus on work that matters. This could be the right place for you if you want to

✓Work on mission-critical AI technology that directly improves clinicians’ day-to-day lives and health system financial health across some of the most complex, high-stakes workflows in the world.

✓Join a “dream team” culture where we hire exceptional people, expect exceptional outcomes and invest deeply in feedback and continuous growth. We operate as a championship team, and that means being ok with hard, uncomfortable, ambiguous problems that lead to real greatness.

✓Operate with real ownership and accountability in an environment where there are no bystanders: If something is broken, we fix it! You will have meaningful autonomy and be expected to drive work to completion.

✓Comprehensive medical, dental, and vision coverage for you and your dependents

✓401(k) with a company match of up to 3% of base salary

✓A remote-friendly culture (with a San Francisco HQ) and full equipment provisioning to ensure you can work effectively from wherever you’re based.

✓Parental leave to support your family needs

✓Annual company-wide off-sites, team off-sites and regular team lunches and all-hands gatherings, with travel, lodging and meals covered

✓Flexible time off with no annual cap, company-wide holidays and an annual holiday shutdown from December 24–January 1 designed to support real rest and long-term sustainability.