Top AI & Machine Learning Jobs in San Francisco, CA

Manage and coach a multidisciplinary engineering team (software, TPM, data science) to deliver cloud and AI-forward customer solutions, act as player‑coach on engagements, drive portfolio and delivery leadership, promote engineering best practices, responsible AI, reusable patterns, and cross‑discipline collaboration.

Design, implement, test, and operate production-quality AI framework components, benchmark and optimize large-model training and inference on GPUs and Microsoft silicon, build automation and observability, drive cross-team projects from definition through deployment, and mentor engineers to improve model onboarding, performance, reliability, and platform efficiency.

Lead vision and roadmap for Brain Health & Detection, using AI/ML and telemetry to improve anomaly detection, signal correlation, alert quality, and automation. Partner with engineering and data science to deliver scalable detection models, set success metrics, drive product execution, influence senior stakeholders, and mentor product managers to advance AIOps capabilities.

Lead cross-functional product strategy and roadmap for Azure HPC & AI compute platform. Use AI-driven research and telemetry to define, prioritize, and launch features, ensure security and compliance, drive go-to-market, and measure product performance across distributed systems and enterprise customers.

Lead design, implementation, and operation of cloud-scale services to improve GPU inference efficiency and utilization across Azure. Translate research into production, build telemetry and control-plane systems, optimize workload placement and capacity, and mentor engineers while collaborating with AI, hardware, and platform teams.

Drive partner-led AI adoption by providing architecture guidance, hands-on enablement, and reference implementations on Microsoft Cloud and Azure ML. Advise on governance, observability, Responsible AI, cost and token economics, and build repeatable, outcome-based AI offerings including agentic, copilot, and LLM-powered solutions.

Lead product and program analytics for Microsoft skilling experiences: define success metrics, analyze learner journeys and funnels, run experiments and causal analyses, build measurement frameworks and competency/skill taxonomies, partner with product and engineering to shape telemetry and reporting, recommend content and curriculum improvements, mentor data scientists, and ensure privacy-compliant analytics.

Lead product strategy and execution for agentic AI tools that scale content creation and maintenance. Define production requirements, collaborate with instructional designers and engineers, integrate with content catalog backends, define success metrics, manage stakeholders and a team, and contribute to Global Skilling strategy and culture.

Lead technical vision and delivery of production-scale measurement and agentic learning systems that personalize pathways, measure learner progress, build skill/knowledge graphs, set causal and experimentation strategies, ensure model reliability and responsible AI, and partner cross-functionally to translate insights into product and business impact.

Design and build compute orchestration and scheduling on Kubernetes and Ray for large-scale AI pretraining. Tune and benchmark GPU (NVIDIA GB200, AMD MIxxx) clusters, improve cluster efficiency, collaborate with research and framework teams, and deliver reliable, observable production infrastructure for conversational AI and foundational model development.