Top AI & Machine Learning Jobs in San Francisco, CA

Lead machine learning engineering efforts to design, build, productionize, monitor, and optimize ML models and systems at scale. Partner with Product and Data Science teams, develop application code, build data pipelines, deploy cloud-based ML solutions, and improve reliability, performance, governance, and responsible AI practices.

Designs, develops, tests, deploys, and supports AI software components for large-scale production systems. Work includes foundation model training, LLM inference, similarity search, guardrails, model evaluation, governance, observability, and optimization of latency, throughput, cost, and scalability. Partners with engineering, research, product, and program teams to deliver responsible AI solutions and shape long-term AI platform strategy.

Data Science PhD interns evaluate modeling and analytics tools, integrate internal and external data, and build data science models for business and customer solutions. The role emphasizes machine learning, deep learning, quantitative analysis, experimentation, and communicating insights to teammates and leadership.

Summer internship focused on data science and machine learning. Interns will evaluate modeling and analytics tools, integrate internal and external data sources, build models, and turn data into actionable insights for customers and leadership. The role emphasizes experimentation, quantitative analysis, and clear communication of technical findings.

Lead design, development, testing, deployment, and support of AI platform components for foundation models and LLM inference. Optimize training and inference performance, build scalable AI systems, and work on similarity search, guardrails, evaluation, governance, and observability. Partner with engineers, researchers, TPMs, and product managers to deliver responsible AI products and technical roadmap initiatives.

Designs, builds, tests, deploys, and supports AI software components for a GenAI platform, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Partners with engineers, researchers, TPMs, and product managers to deliver production AI systems and improve scalability, latency, throughput, cost, and hardware utilization.

Architects and scales AI compute infrastructure for Capital One’s machine learning platform, supporting distributed training, inference, and data processing workloads. Builds secure multi-tenant control and data plane systems, improves operational reliability, automates KTLO work, and optimizes compute economics. Leads technical execution across distributed systems, collaborates with product and program teams, mentors engineers, and helps recruit and elevate the engineering organization.

Build and deploy production AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Partner with engineers, researchers, TPMs, and product managers to deliver scalable, responsible AI solutions on cloud platforms, optimize performance and cost, and help define the technical vision for Capital One’s foundational AI infrastructure.

Lead AI/ML engineering for Capital One’s AI platform, building and supporting scalable AI software components such as foundation model training, LLM inference, similarity search, guardrails, experimentation, governance, and observability. The role also focuses on optimizing training and inference performance, partnering with engineering, research, TPM, and product teams, and shaping the long-term technical vision for foundational AI systems.

Lead applied research teams building and scaling AI foundation models for banking products. Partner with scientists, engineers, and product leaders to develop, train, evaluate, validate, and implement models. Guide and mentor applied scientists, represent Capital One in the research community, and translate research into business-impacting AI experiences.