Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Lead ML Engineer responsible for designing, building, deploying, and maintaining production ML systems and data pipelines at scale. Collaborate with product and data science teams, ensure model governance, monitoring, CI/CD, and leverage cloud-based architectures to deliver performant, explainable ML solutions.

The role involves designing, developing, and optimizing AI software components, collaborating with cross-functional teams, and contributing to the technical vision of AI systems at Capital One.

Partner with teams to deliver AI-powered products, build foundation models, and conduct applied research to enhance customer experiences.

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.

Lead and deliver large, cross-functional technical programs for Card Tech, align stakeholders, provide technical leadership, build TPM discipline, drive platform and automation strategy, and standardize migrations to core systems.

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.

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 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.