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Salary: USD 225,000 - 300,000 / annual
Ambience Healthcare is building an AI intelligence platform that restores humanity to healthcare by removing administrative burden from clinicians. The company delivers real-time, coding-aware documentation and clinical workflow support across ambulatory, emergency, and inpatient settings at top health systems in North America. Backed by leading VCs including a16z, OpenAI Startup Fund, and Kleiner Perkins, Ambience has been recognized as #1 for improving clinician experience by KLAS Research and named a LinkedIn Top Startup.
As a Senior Machine Learning Engineer, you will own complex AI projects end-to-end, from diagnosing production failures to designing evaluations and building agentic systems. This is a highly hands-on role requiring significant technical ownership and close collaboration with clinicians, product managers, and engineers.
Key responsibilities include: designing and owning evaluation pipelines for LLM and agentic systems using automated graders, regression testing, production feedback, and human evaluation; diagnosing high-impact failure modes and testing improvements across prompting, retrieval, context, routing, data, and fine-tuning; developing production agentic systems involving tool use, retrieval, state management, and failure recovery; building data and improvement flywheels from production failures and user feedback; staying current with cutting-edge research in LLMs, agents, NLP, speech, and multimodal AI; and owning AI systems end-to-end across models, data, evaluation, orchestration, serving, and observability.
Required qualifications: 5+ years in production ML, research engineering, or applied AI with demonstrated experience building consequential production AI systems or materially improving model behavior in production. Deep understanding of modern LLMs, transformers, and production AI systems. Extensive experience designing evaluations for LLMs, agents, or complex AI systems, with ability to translate ambiguous quality problems into measurable dimensions and experiments. Experience building production systems involving multiple models, tools, retrieval, context, state, routing, or orchestration. Proficiency in Python and modern ML frameworks (PyTorch preferred), with comfort in deployment, observability, CI/CD, and containerized systems. Strong data-centric AI development skills and ability to work effectively across disciplines with clinicians, product managers, and engineers.
Nice-to-haves include experience with realtime voice, conversational AI, multimodal systems, fine-tuning, post-training, model adaptation, healthcare or regulated industries, mentoring ML engineers, and open-source contributions to ML tooling.
The role is hybrid, requiring three days per week onsite at the San Francisco office.
