Top AI & Machine Learning Jobs in San Francisco, CA

Design, develop, deploy, and optimize responsible generative AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Lead orchestration and cost-performance optimization across production AI platforms, establish governance and engineering standards, contribute to long-term technical roadmaps, and mentor senior AI engineering staff.

Lead the design, development, and operation of Capital One’s generative AI training platform, including distributed training, reinforcement learning, GPU scheduling, fault tolerance, model experimentation, and observability. Own GPU capacity planning, cost governance, technical strategy, responsible AI standards, and enterprise execution. Build and lead a multi-team AI engineering organization while partnering with research, product, compliance, and risk teams.

Lead applied AI research and define long-term research directions for foundation models and other large-scale machine learning systems. Build, train, evaluate, validate, and deploy models using large datasets and high-volume inference infrastructure. Collaborate with data scientists, engineers, and product leaders to deliver AI-powered products, mentor scientists and managers without direct reporting responsibility, represent Capital One externally, and guide multi-year research programs.

Leads applied AI research and strategic direction for foundation models and AI-powered products. Responsibilities include designing, training, evaluating, validating, and deploying large-scale models; advancing research in areas such as LLMs, deep learning, optimization, and robustness; mentoring scientists and managers; collaborating with engineering, product, and business leaders; and representing Capital One within the external research community.

Build and lead scalable AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize models and infrastructure for cost, latency, throughput, and hardware utilization. Architect multi-model orchestration pipelines, establish AI performance governance, guide technical roadmaps, lead design reviews, and mentor senior AI engineers.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, lead multi-model orchestration, promote responsible AI, and mentor engineering teams while influencing technical strategy and cross-functional stakeholders.

Leads data science strategy and development for agentic AI products. Builds, validates, deploys, and optimizes machine learning models using large-scale structured and textual data. Designs A/B tests, analyzes telemetry, translates findings into enterprise AI strategy, and partners with data scientists, engineers, and product managers. The role also requires technical leadership, stakeholder communication, talent development, and innovation across machine learning and statistical modeling.

Designs and leads enterprise-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establishes AI architecture standards, optimizes model performance and infrastructure costs, and supports hybrid and multi-cloud deployments. The role also drives responsible AI practices, technical roadmaps, cross-functional influence, mentoring, and research-to-production delivery.

Design, develop, deploy, and optimize large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, improve model performance and infrastructure efficiency, lead multi-cloud strategies, champion responsible AI, and mentor engineering teams and stakeholders.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization. Architect multi-model orchestration pipelines, establish AI engineering standards, lead design reviews, contribute to technical roadmaps, and mentor senior AI engineers.