Top AI & Machine Learning Jobs in San Francisco, CA

Drive technical sales and adoption of Microsoft AI and cloud platforms through PoCs, demos, hackathons, and architecture workshops. Design secure, scalable AI application architectures, resolve technical blockers, collaborate with engineering, and provide developer-facing thought leadership to accelerate production deployments and developer productivity.

Lead a cross-discipline engineering and data science team in a Forward Deployed Engineering model, owning delivery from problem definition to production. Coach and develop talent, drive portfolio and delivery leadership, model engineering excellence, and apply AI-forward practices, reusable patterns, and responsible AI to deliver customer outcomes.

Work with researchers and engineers to implement and optimize generative AI inference systems. Build tools for debugging performance, profiling, distributed systems, and improve compute efficiency across large heterogeneous GPU fleets. Deliver end-to-end solutions and processes to accelerate research and production inference.

Customer-facing Cloud Solution Architect who designs and deploys secure, scalable, AI-ready data platforms on Azure. Leads architecture sessions, proofs-of-concept, and production deployments, ensuring performance, resiliency, and security. Drives adoption, identifies expansion opportunities, enables partners, contributes to IP and technical readiness, and represents Microsoft in technical communities and events.

Lead end-to-end delivery of high-impact data science and AI projects: translate business needs, prepare data, build and deploy predictive, prescriptive, and generative AI solutions (including LLM fine-tuning and prompt engineering), ensure production readiness and responsible AI, and advise stakeholders to drive adoption.

Lead data science strategy across complex customer engagements: frame business problems, define modeling and data readiness standards, produce reusable operational ML patterns, provide hands-on coding and MLOps expertise, mentor teams, and influence executive stakeholders to drive measurable business impact and responsible AI practices.