Top AI & Machine Learning Jobs in San Francisco, CA

Leads the technical vision, architecture, and execution of AI performance initiatives across frameworks, compilers, runtimes, systems, services, and hardware. Drives benchmarking, automation, observability, model performance, reliability, and Azure capacity efficiency. Aligns cross-functional teams, mentors engineers, resolves complex technical tradeoffs, and remains hands-on with prototyping, implementation, debugging, design reviews, and operational readiness.

Build and optimize distributed services supporting large-scale reinforcement learning, LLM post-training, and production AI workloads. Improve researcher and engineer iteration loops, debug model-hardware interactions, enhance performance and reliability, and develop expertise in ML systems, AI infrastructure, compute orchestration, GPU computing, inference, and distributed training. Responsibilities also include secure coding, architecture, deployment, observability, incident response, and on-call support.

Own end-to-end data science and AI projects for Microsoft’s AI infrastructure. Analyze fleet-scale telemetry and experimentation data, build scalable predictive and prescriptive models, and translate insights into reliability, efficiency, capacity-planning, and customer outcomes. Partner with engineers, stakeholders, and customers to define problems, communicate findings, guide adoption, and promote responsible AI practices. The role also requires production ML deployment, software engineering practices, and consulting influence.

Lead customer-facing data science engagements: oversee data acquisition/cleaning, build and evaluate scalable models, drive best practices, write extensible code, and define solution strategy while collaborating with engineers and product teams.

Customer-facing Cloud Solution Architect responsible for designing, delivering, and operationalizing AI-powered applications on Azure. Lead architecture, POCs, MVPs, and production readiness for Azure AI, AKS, App Service, APIM, Serverless, Containers, and GitHub. Drive customer adoption, consumption growth, resiliency, security, and optimization while providing technical workshops, mentoring, and feedback to engineering teams.

Lead technical strategy with priority partners across ASEAN to build repeatable Microsoft Cloud and AI services. Translate business priorities into technical roadmaps, run innovation workshops, guide architecture/design, validate offers, enable partner readiness, and drive co-sell execution to convert technical work into measurable revenue and consumption.

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.