Top AI Research Jobs in San Francisco Bay Area, CA

The Applied Research Engineer will enhance AI systems through research and experimentation, focusing on information retrieval and reasoning strategies, collaborating with engineering teams to transition validated methods into production.

The role involves leading AI research projects, building AI foundation models, collaborating with engineering teams, and mentoring applied scientists to enhance customer interactions through AI.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, run causal and bandit experiments, decompose merchant economics, and develop agentic deal tooling. Own end-to-end data pipelines, ETL, monitoring, and visualization. Evaluate AI systems for accuracy, bias, and drift, and partner with cross-functional teams to translate results into pricing strategy and automation.

As a Staff Applied AI Scientist, you will design and deploy AI systems using LLMs, improve agent performance, and collaborate with engineering to enhance systems for customer impact.

Graduate research interns will own end-to-end projects developing customer world models and proactive intelligence. Work spans representation learning, foundation models, reinforcement learning, agentic decision systems, and evaluation. Tasks include designing methods, running experiments, publishing findings, and potentially shipping models into production to impact customer-facing products.

Conduct foundational and applied ML research on AI well-being and moral status, design and run experiments (including tooling/infrastructure), collaborate with external researchers, and publish papers, blog posts, and talks to inform AI developers, policymakers, and the public.

Lead development and evaluation of AI products for medical professionals using large-scale datasets. Create product and client-facing analytics, influence data team strategy, and collaborate with product managers, analysts, and engineers to execute data projects from ingestion through analysis to recommendations.

Lead MongoDB's strategy to increase LLM and answer-engine visibility by optimizing content, structured data, and community signals. Build and scale an AEO/SEO team, implement technical information architecture (JSON-LD, knowledge graphs, RAG-friendly docs), coordinate UGC/community advocacy, evangelize AI-first content practices, and develop analytics/attribution (Share of Model, citation impact) to drive PLG acquisition.

Lead and run AI advisory engagements: conduct market research, impact sizing, synthesis, and workshops; manage timelines and cross-functional coordination; build reusable playbooks and AI-powered automation to accelerate delivery and commercial expansion.

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.