Top AI & Machine Learning Jobs in San Francisco, CA

Apply statistical inference and experimentation to evaluate product changes, identify Growth opportunities, and guide strategy. Partner with product, engineering, and marketing leaders to define metrics, build datasets and dashboards, and communicate insights that drive business and product decisions. The role requires SQL, scripting, data transformation, and practical tradeoffs between speed and accuracy. Predictive modeling experience is preferred, as are startup and B2B SaaS backgrounds.

The AI Applications Engineer will prototype AI-driven solutions, collaborate with stakeholders, and create reusable components to address business challenges.

As an AI Product Engineer, you'll build and scale AI-powered features for Notion's Custom Agents, collaborating with teams to enhance user experience and implementing cutting-edge technologies.

Conduct data analyses, identify product and business improvement opportunities, communicate actionable recommendations, build metrics and dashboards, and support strategic decisions. The intern will collaborate cross-functionally with product and business teams, using SQL and scripting languages such as Python or R. The role is based in Notion’s San Francisco office from January 25 through April 16, 2027.

Work on AI-powered Notion features: prototype and ship model-powered product improvements, integrate and productionize models, improve model performance and reliability, and build evaluation frameworks and experiments to measure and improve quality.

Lead UX research to define and scale evaluation of Notion's AI experiences. Create reusable rubrics and measurement approaches, run longitudinal and feature-specific studies, identify failure modes and recovery behaviors, and operationalize evaluation with product, design, engineering, and data science partners to improve model output quality and end-to-end user experience.

Build and scale core AI platform primitives and integrations to enable rapid, safe AI product development. Own infrastructure, shared libraries, APIs, observability, and reliability systems; operate production AI systems, debug provider/model behavior, and implement versioning, rollout controls, and quality gates to minimize user disruption.