Top AI Research Jobs in San Francisco Bay Area, CA

Build and deploy AI capabilities for Hex’s Notebook Agent and context engine. Responsibilities include running experiments, fine-tuning models, developing AI infrastructure and experimentation tooling, architecting AI-enabled systems, and shipping production experiences for data scientists and analysts. The role partners closely with product teams, spans greenfield initiatives, and offers opportunities for technical and team leadership.

Leads applied AI research and strategic direction for foundation models and large-scale machine learning systems. Responsibilities include designing, training, evaluating, validating, and deploying models; advancing research in areas such as LLMs, optimization, self-supervised learning, robustness, and explainability; guiding applied scientists and managers; collaborating with engineering, product, and academic partners; and translating research into scalable customer-facing products.

Leads applied AI research and strategic direction for foundation models and large-scale machine learning systems. Partners with scientists, engineers, and product leaders to design, train, evaluate, validate, and deploy AI models. Defines long-term research priorities, advances state-of-the-art methods, mentors applied scientists and managers, collaborates with academia, and represents Capital One in the research community.

Graduate research interns will own end-to-end projects to build customer world models and proactive intelligence: developing representation learning, foundation model, RL/agentic methods, evaluation frameworks, and shipping research into production to improve customer decision-making.

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.

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 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.