Top AI & Machine Learning Jobs in San Francisco, CA

Design and implement scalable microservices and systems that use deep learning, anomaly detection, and distributed data processing to autonomously analyze Azure platform telemetry, drive reliability, and enable self-recovery. Deliver well-tested features, collaborate across teams, and apply engineering best practices.

Lead product and program analytics for Microsoft skilling experiences: analyze learner journeys and funnels, define success metrics, build measurement frameworks, run experiments and causal analyses, surface opportunities to improve engagement and proficiency, shape telemetry and scalable reporting with engineering, develop competency and skill taxonomies, mentor data scientists, and ensure analyses meet privacy requirements.

Lead hardware-software co-design for datacenter-scale AI systems, analyze workloads to drive architectural decisions, optimize parallelism and runtimes, develop performance models, prototype and productionize co-design ideas, influence hardware and platform roadmaps, and mentor senior engineers to improve system performance, scalability, and cost efficiency.

Lead product efforts for Azure HPC & AI supercomputers by engaging customers, partnering with SREs and developers, analyzing telemetry and user data, driving tooling/features and iterative service improvements, coordinating with partners/integrators, and embedding security-by-default to improve availability, reliability, and customer experience.

Design, implement, and ensure quality of systems-level software, tools, and SDK integration for AI frameworks. Collaborate with hardware, ML, and platform teams; write high-performance code (C/C++, C#, Python); define architectures, coding standards, and schedules; and work on device interfaces, runtime systems, and observability/profiling.

Build and maintain cloud-native AI/HPC tooling and services for deployment, monitoring, profiling, and debugging at hyperscale. Drive architecture, establish best practices, act as DRI for incidents, and improve reliability, performance, and observability of supercomputing infrastructure.

Lead and develop a team of Copilot and Cloud Endpoint Solution Engineers to drive customer AI adoption, shape solution architecture, execute strategic engagements, and deliver measurable business outcomes through technical pre-sales, stakeholder influence, and operational management.

Design and lead enterprise data and GenAI architectures, translate business outcomes into scalable secure solutions, drive migration/modernization, define GenAI/Copilot/RAG patterns, establish MLOps/LLMOps, ensure Responsible AI and governance, and act as trusted technical advisor across presales to production.

Develop and iterate post-training recipes for large language models: drive data collection and acquisition, build capability evaluations, design experiments, and apply reward modeling and RL to improve model performance for reasoning, instruction following, code, and tool use.

Develop and optimize AI compilers and code generation for new hardware, using LLVM/MLIR, Torch Dynamo, and Triton. Implement compiler features and optimizations in C++ and Python, improve AI workload performance, design programming abstractions, collaborate with hardware and ML teams, and plan and deliver engineering solutions.