Top AI & Machine Learning Jobs in San Francisco, CA

Research and develop large-scale AI foundation models (design, training, evaluation, validation, deployment). Collaborate with engineers, product and ML teams to apply state-of-the-art methods, optimize training/inference, and translate research into production AI-powered products. Own research agendas and deliver reproducible results and publications.

Lead design, development, deployment, and support of foundational AI systems including LLM training and inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI performance (latency, throughput, cost), collaborate cross-functionally, and contribute to technical vision and roadmap for production AI infrastructure.

The Senior Lead AI Engineer at Capital One will design, deploy, and support AI systems, working with cross-functional teams to innovate and enhance customer interactions while ensuring scalability and performance.

Lead a cross-functional team to deliver AI-powered banking products, focusing on developing and supporting AI software components and optimizing LLM technologies.

Design and build an enterprise generative AI platform: agent orchestration, memory, guardrails, vector search, SDKs, and blueprints. Standardize agentic workflows, optimize orchestration and cost, own Helm/Kubernetes operators, ensure trust and safety, mentor engineering teams, and evangelize platform architecture internally and externally.

Lead design and delivery of an agentic AI platform: define architecture, standardize agentic workflows, build GenAI SDK/CLI, harden guardrails and observability, optimize orchestration and costs, own Helm/K8s operators for autoscaling, incubate RFCs and proofs of concept, and mentor engineering leaders while evangelizing platform practices internally and externally.

Lead enterprise-scale Generative AI programs from research to production, focusing on multi-agent conversational systems, agentic architectures, pretraining/finetuning, and inference optimization. Drive cross-functional delivery, manage technical risk, establish evaluation and deployment frameworks, partner with risk/compliance, and ensure scalable, secure adoption of AI solutions.

Ten-week, full-time paid Data Science internship (June–August 2027) working on machine learning, deep learning, and large-scale data analysis. Interns evaluate modeling tools, integrate internal and external data/APIs, design data science models, and present insights to stakeholders. In-person attendance at an assigned U.S. location is required; interns may be considered for full-time roles after graduation.

Ten-week, full-time paid Data Science internship for current PhD students. Interns will build and evaluate modeling and analytics tools, integrate internal and external data sources, design data science models, and communicate results. Candidates must continue their PhD after the internship and may be evaluated for full-time roles upon graduation.

Lead design, build, deploy, and support foundational AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and optimization. Partner with cross-functional teams to deliver scalable, performant, and responsible generative AI platform services and contribute to long-term technical vision and roadmap.