Top AI & Machine Learning Jobs in San Francisco, CA

Lead development of foundational AI systems, including model training, LLM inference, guardrails, evaluation, governance, and observability. Partner with engineers, scientists, TPMs, and product managers to build scalable, responsible AI products. Optimize production AI performance for latency, throughput, cost, and hardware utilization while contributing to the long-term technical vision.

Build and optimize enterprise AI systems, including foundation models and LLM inference, for Capital One. Design, deploy, and support scalable, responsible AI components using cloud and open-source technologies, improve performance and cost, and help define the long-term AI roadmap.

Conduct applied AI research to build and improve foundation models for customer-facing products. Partner with data scientists, engineers, ML engineers, and product managers to design, train, evaluate, validate, and deploy scalable AI systems. Use modern deep learning and cloud technologies, translate research into business impact, and pursue an independent research agenda with publications and innovations.

Partners with cross-functional teams to research, develop, train, validate, and implement AI foundation models. Uses PyTorch, AWS, Hugging Face, Lightning, and vector databases to build scalable AI systems, improve customer experiences, and translate research into business impact.

Leads applied AI research and foundation model development for banking products. Partners with scientists, engineers, and product leaders to design, train, evaluate, validate, and implement large-scale AI systems. Represents Capital One in the research community, mentors applied scientists, and turns cutting-edge research into production AI experiences using tools like PyTorch, AWS, Hugging Face, and vector databases.

Conduct applied AI research focused on foundation models, partnering with data scientists, software engineers, machine learning engineers, and product managers to build trustworthy AI systems. Develop, train, evaluate, validate, and implement large models using tools like PyTorch, Hugging Face, Lightning, AWS UltraClusters, and vector databases. Translate research into scalable customer-facing products, contribute to libraries and platform code, and pursue an independent research agenda with high-impact outcomes.

Designs, develops, tests, deploys, and supports generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimizes large-scale AI systems for performance, latency, throughput, cost, and hardware utilization while partnering with engineers, scientists, TPMs, and product managers.

Lead development of AI systems and infrastructure for foundation models, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Partner with engineering, research, TPM, and product teams to deliver scalable, responsible AI products and optimize training and inference performance for production use.

Designs, develops, tests, deploys, and supports AI foundations and LLM systems, including model training, inference, similarity search, guardrails, evaluation, governance, and observability. Optimizes large-scale production AI for latency, throughput, cost, and hardware utilization. Partners with engineers, researchers, TPMs, and product managers to build responsible, scalable AI capabilities and influence the long-term AI roadmap.

Lead applied AI research and foundation model development, partnering with scientists, engineers, and product teams to build scalable AI-powered customer experiences.