Top AI & Machine Learning Jobs in San Francisco, CA

Partner with product, engineering, design, ops, and clinical teams to define metrics and experiments. Design and analyze A/B tests and quasi-experiments, apply causal inference when needed, perform funnel/cohort analyses, build and validate predictive models, and operationalize insights with monitoring and launch criteria. Produce clear, decision-ready analyses and raise organizational standards for metrics and experimentation.

Lead development of AI-powered patient experiences: LLM-driven intake and therapist matching, between-session support, evaluation and safety infrastructure, outcome measurement, and HIPAA-compliant product delivery. Partner with Engineering, ML, Data Science, Clinical, and Legal to ship and monitor models in production, write evals, design red-teaming and human-in-the-loop flows, and measure patient outcomes.

Lead Headway's AI platform vision and roadmap, own the internal assistant Eddy, write evals and prompts, configure agents and integrations, and partner with CX, Ops, Sales, Finance, and Clinical teams to identify high-value AI workflows and drive adoption across the company.

Lead measurement and decision systems for patient growth: own incrementality, design experiments, build LTV/retention/forecasting models, create trusted marketing metrics, partner with Engineering for measurement plumbing, diagnose performance shifts, influence channel allocation, and mentor growth analytics teams.

Lead product measurement strategy and complex evaluation for major product areas. Own experimentation standards, design causal and Bayesian analyses, build scalable measurement systems and dashboards, drive cross-team initiatives, mentor data scientists, and influence product decisions to improve mental healthcare outcomes.

Build and architect LLM/ML-powered systems to evaluate clinical quality, detect risk in claims, match patients to providers, and ensure trustworthy AI. Lead technical foundations, mentor engineers, ship end-to-end product features across frontend and backend, and create evaluation and observability for safe, scalable healthcare ML.