Top AI & Machine Learning Jobs in San Francisco, CA

Lead design, build, deploy, and support scalable, responsible AI systems (foundation model training, LLM inference, similarity search, guardrails, observability). Collaborate with cross-functional teams, optimize model performance and cost, and shape technical vision and roadmap for foundational AI platform services.

Drive applied research to build and deploy AI foundation models end-to-end. Partner with cross-functional teams to design, train, evaluate, validate, and implement large-scale models using open-source tools and cloud infrastructure. Mentor scientists, translate research into business goals, and publish/represent Capital One in the research community.

As a Manager in Data Science, you'll lead the development of machine learning models, partner with engineering and product teams, and analyze large datasets to enhance customer experiences and prevent fraud.

Lead an Agile team to design, build, deploy, and maintain production-scale ML models and pipelines. Drive ML architecture, model training, CI/CD, monitoring, and cloud/Kubernetes-based deployments while ensuring responsible, explainable, and well-governed ML solutions.

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

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.

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

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.

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.