Top AI & Machine Learning Jobs in San Francisco, CA

The role involves leading AI solutions development, optimizing AI systems, and collaborating with cross-functional teams to enhance customer interactions at Capital One.

In this role, you will design, develop, and support AI software components, collaborate with cross-functional teams, and contribute to the roadmap for foundational AI systems.

The Senior Director, Applied Research leads teams to develop AI solutions, partners with cross-functional groups, and engages in high-impact applied research.

The Director of AI Engineering is responsible for strategic vision, development, and management of AI systems at Capital One, leading teams to deliver scalable AI solutions aligned with business goals while ensuring compliance with ethical standards.

The Applied Researcher II will develop AI-powered products, partner with cross-functional teams, engage in applied research, and implement AI foundation models.

Lead ML engineer responsible for designing, building, deploying, and monitoring production ML systems at scale. Collaborate with product and data science teams, develop ML architectures and data pipelines, apply CI/CD and cloud-based MLOps, retrain and govern models, and ensure performance, reliability, and responsible/explainable AI practices.

Lead design, build, and productionize scalable ML models and pipelines. Collaborate with product and data science teams, implement cloud-based architectures, ensure CI/CD, monitoring, model governance, and responsible AI practices.

As a Lead AI Engineer, you will design, develop, and support AI software components while partnering with cross-functional teams to deliver AI-powered products at Capital One.

The Senior Lead AI Engineer will develop and support AI components, lead a cross-functional team, and drive innovation in AI systems at Capital One.

Lead an AI engineering team to design, build, deploy, and optimize large-scale generative AI systems. Oversee foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and make strategic build-vs-buy decisions. Drive technical vision, roadmap, and talent development while ensuring scalable, cost-efficient, and responsible AI production systems.