Top AI & Machine Learning Jobs in San Francisco, CA

Convert ambiguous healthcare and operational problems into well-scoped ML/AI, ranking, optimization, NLP, or LLM tasks; build and evaluate models, run error analysis, design honest evaluations, partner with engineering and clinical stakeholders, and productionize reliable models with clear communication of tradeoffs and limitations.

Drive company-wide AI adoption by embedding with cross-functional teams to identify use cases and build production AI workflows, agents, internal tools, prompt/skill libraries, evaluation frameworks, and safe deployment patterns. Provide training, office hours, and enablement while partnering with security, IT, legal, and clinical stakeholders to ensure PHI-safe guardrails and measure adoption, productivity, and quality improvements.

Founding ML engineering hire to design and build production ML systems: training and inference pipelines, serving, feature workflows, monitoring, retraining, governance, and operational practices. Partner across engineering, data, product, and applied science to productionize models, implement observability, prevent degradation, and set platform standards and hiring bar for future ML engineers.

Lead and conduct ML research aligned to Sprinter Health's strategy: define research agenda, design rigorous experiments, develop novel methods and evaluations, publish and patent findings, translate research into production-ready tools, and collaborate with clinicians and cross-functional teams to validate and deploy clinically robust AI solutions.

Design and deploy optimization, forecasting, and simulation models to match clinicians to patients under constraints. Build baselines, run evaluations and experiments, and productionize decision systems in partnership with engineering, product, and operations to improve operational metrics like cost per visit and clinician utilization.