Top AI & Machine Learning Jobs in San Francisco, CA

Design, develop, deploy, and support large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Lead technical reviews and governance, define AI platform roadmaps, integrate heterogeneous AI systems, and mentor senior engineering talent.

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.

Design, develop, deploy, and optimize production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead multi-model orchestration and cost-performance optimization across cloud infrastructure. Establish AI engineering standards, guide technical roadmaps, lead design reviews, and mentor senior AI engineers while integrating research advancements into scalable, responsible products.

Design, develop, deploy, and support enterprise AI systems, including foundation models, LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Establish AI architecture standards, optimize model performance, cost, latency, and scalability, and lead model routing and orchestration initiatives. Drive responsible AI practices, contribute to technical roadmaps, mentor engineering teams, and influence cross-functional stakeholders and enterprise AI strategy.

Leads data science for enterprise agentic AI products, developing and deploying machine learning models, designing experiments, analyzing telemetry, and translating findings into business strategy. Partners with data scientists, AI/ML engineers, and product managers while guiding large-scale analysis across numeric and textual data. The role also requires technical leadership, stakeholder communication, innovation, talent development, and expertise in statistical modeling, deep learning, and cloud-based data science.

Designs, develops, deploys, and optimizes enterprise AI systems, including foundation models, LLM inference, agentic workflows, similarity search, model routing, and observability. Establishes AI architecture standards, improves system cost and performance, champions responsible AI, and leads technical direction, mentorship, and cross-functional collaboration across Capital One.

Design and deliver enterprise-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Set architecture and optimization strategies for scalable, cost-efficient, responsible AI platforms. Lead model routing, caching, orchestration, and cloud deployments while mentoring engineers, influencing stakeholders, and advancing AI research into production.

Lead the design, development, deployment, and operation of machine learning models and platforms at scale. Build distributed ML infrastructure, data pipelines, cloud-based architectures, and production services. Develop and test application code, automate deployment, monitor and retrain models, and apply CI/CD, governance, security, and responsible AI practices. Collaborate with Product, Data Science, and Agile teams to solve complex business problems.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, cost-performance governance, optimization, technical design reviews, and responsible AI standards. Partner with engineering, research, product, and program teams while mentoring senior technical staff and shaping the roadmap for foundational AI platforms.