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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer - **Company:** Ambience Healthcare - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $225,000.0 - $300,000.0 - **Contract:** Permanent contract - **Skills:** AI Evaluation, Artificial Intelligence, Continuous Integration, Software Debugging, Python (Programming Language), Machine Learning, Routing, Regression Testing, Tensorflow, Chatbots, Pytorch, Evaluation Pipelines, Large Language Models, Agentic-AI, Free and Open-Source Software, Machine Learning Operations, Automation Anywhere - **Published:** October 27, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-machine-learning-engineer-san-francisco-ca--fe61c4d8-951d-4f70-a6cd-4a2603790531 ## About the Role Machine Learning, Healthcare, Return on Investment (ROI), Artificial Intelligence (AI), LinkedIn, Startup, Identify Issues, Machine Learning, Medical Products, Failure Analysis, * 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. ## Description 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.