Top AI Research Jobs in San Francisco Bay Area, CA

Conduct applied research and develop large-scale AI foundation models, including vision-language models, across design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver AI-powered products. Research optimization, self-supervised learning, robustness, explainability, and fine-tuning methods, while producing technical reports and conference submissions.

Leads Deepgram’s end-to-end Text-to-Speech research program, owning research strategy, technical direction, model development, evaluation, and production deployment. Advances neural audio modeling, prosody, expressiveness, multilingual generation, voice consistency, controllability, and inference performance. Builds and develops research teams, manages technical leaders, prioritizes experiments and compute, evaluates model quality, and partners with engineering and product leadership to ship production-grade models.

Own the measurement and improvement of model learning from tasks. Collaborate with frontier labs to design post-training recipes and data-quality techniques, run in-house evaluation systems, develop datasets and data products, assess acquired data, and build scalable ingestion and evaluation systems. The role requires production reinforcement learning experience at a frontier lab and end-to-end LLM post-training expertise.

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 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.

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 AI-focused marketing strategy and ecosystem to position MongoDB as the default data platform for agentic applications. Own AI narrative, visibility in AI systems, ecosystem integrations and partnerships, documentation and training-data accuracy standards, and acquisition channels for agentic builders. Drive cross-functional alignment and measurable outcomes with product, engineering, and partner teams.

The Director of Developer Relations leads the strategy for engaging developers around CoreWeave's AI SaaS offerings, creating content, fostering community, and improving the onboarding experience.

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.

Lead long-term product strategy and commercialization for Physical AI data products. Collaborate with researchers, ML engineers, operations, and GTM to identify market opportunities, build business cases, conduct customer and competitive research, and drive cross-functional initiatives. Hire, coach, and manage product teams while delivering data-driven strategies to scale revenue and support customer research needs. Role based in San Francisco with up to 30% travel.