Job description

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 agentic systems, combining automated graders, regression testing, production feedback, and human evaluation to measure real product quality. - Improve Production Model Behavior: Diagnose high-impact failure modes and test improvements across prompting, retrieval, context, routing, data, fine-tuning, or other model and system interventions. - Build Agentic AI Systems: Develop production systems involving tool use, retrieval, context and state management, routing, orchestration, tracing, and failure recovery. - Build Data and Improvement Flywheels: Turn production failures and user feedback into better datasets, evaluations, and model behavior through active learning and systematic iteration. - Stay at the Cutting Edge: Distill insights from recent research in LLMs, agents, NLP, speech, and multimodal AI and translate promising ideas into practical experiments. - Own AI Systems End-to-End: Work across models, data, evaluation, orchestration, serving, and observability, while remaining deeply hands-on in code and production debugging. WHO YOU ARE: - Strong Production AI Experience 5+ years in production ML, research engineering, or applied AI. Have built a consequential production AI system or materially improved model behavior in production. Strong understanding of modern LLMs, transformers, and production AI systems. - Deep Evaluation Experience Experienced designing evaluations for LLMs, agents, or other complex AI systems. Can turn ambiguous quality problems into measurable dimensions, datasets, and experiments. Familiar with challenges such as grader bias, leakage, misleading aggregate metrics, regression detection, and offline-online mismatch. - Agentic Systems Experience Experience building production systems involving multiple models, tools, retrieval, context, state, routing, or orchestration. Understands reliability and failure modes in complex AI workflows, not just individual model calls. - Production-Grade Software Engineer Proficient in Python and modern ML frameworks; PyTorch preferred. Comfortable with deployment, observability, CI/CD, and containerized systems. Still highly hands-on: writes code, inspects traces, analyzes failures, and debugs production systems. - Data-Centric AI Developer Skilled at building high-quality datasets and feedback loops. Experienced using production failures, user feedback, and active learning to improve model and system quality. - Effective Interdisciplinary Collaborator Able to work closely with clinicians, product managers, and fellow engineers. Strong communicator who can simplify complex AI concepts for diverse audiences. Comfortable owning ambiguous technical problems and driving them to measurable outcomes. Nice-to-Haves - 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. Life at Ambience 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. To help you do your best work, we pair these expectations with benefits intentionally designed to help you feel supported and safe at Ambience and beyond. Some of our key benefits include - 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. Ambience Healthcare is an equal opportunity employer and is committed to building a diverse and inclusive workplace. We do not discriminate on the basis of race, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, disability, veteran status, genetic information, or any other legally protected status. We encourage applicants from all backgrounds to apply. Ambience is committed to supporting every candidate’s ability to fully participate in our hiring process. If you need any accommodations during your application or interviews, please reach out to our Recruiting team at accommodations@ambiencehealthcare.com recruiting@ambiencehealth.com. We’ll handle your request confidentially and work with you to ensure an accessible and equitable experience for all candidates. Ambience Healthcare has become aware of scams targeting jobseekers with fake jobs and even interviewing people. Our emails will always come from @ambiencehealthcare.com. We would never our ask candidates to download apps or make any form of payment(s). If you are contacted through WhatsApp, Telegram, similar but fake email domains, or asked to make a payment, these contacts are not legitimate. Report the issue immediately to LinkedIn and the FBI fbi.gov.

Requirements

Must-haves

Our engineering roles are hybrid — working onsite at our San Francisco office three days per week.
Improve Production Model Behavior: Diagnose high-impact failure modes and test improvements across prompting, retrieval, context, routing, data, fine-tuning, or other model and system interventions.
Build Agentic AI Systems: Develop production systems involving tool use, retrieval, context and state management, routing, orchestration, tracing, and failure recovery.
Build Data and Improvement Flywheels: Turn production failures and user feedback into better datasets, evaluations, and model behavior through active learning and systematic iteration.
Stay at the Cutting Edge: Distill insights from recent research in LLMs, agents, NLP, speech, and multimodal AI and translate promising ideas into practical experiments.
Own AI Systems End-to-End: Work across models, data, evaluation, orchestration, serving, and observability, while remaining deeply hands-on in code and production debugging.
Strong Production AI Experience
5+ years in production ML, research engineering, or applied AI.
Have built a consequential production AI system or materially improved model behavior in production.
Strong understanding of modern LLMs, transformers, and production AI systems.
Deep Evaluation Experience
Experienced designing evaluations for LLMs, agents, or other complex AI systems.
Can turn ambiguous quality problems into measurable dimensions, datasets, and experiments.
Familiar with challenges such as grader bias, leakage, misleading aggregate metrics, regression detection, and offline-online mismatch.
Agentic Systems Experience
Experience building production systems involving multiple models, tools, retrieval, context, state, routing, or orchestration.
Understands reliability and failure modes in complex AI workflows, not just individual model calls.
Production-Grade Software Engineer

Nice-to-haves

Proficient in Python and modern ML frameworks; PyTorch preferred.
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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningllmmlpythonci/cdrest

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Parental Leave