Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Design, deploy, operate, and tune large-scale HPC environments for AI model training. Own HPC domains (GPU compute, storage, networking), manage schedulers (SLURM, Kubernetes), build automation in Bash/Python, troubleshoot cluster performance, and partner with researchers to support high-scale training workloads.

Lead product efforts for Azure HPC & AI supercomputers by engaging customers, prioritizing backlog from telemetry and incidents, partnering with SREs and engineers to improve service health, build deployment strategies, and deliver AI-driven product improvements with security-by-default.

Lead hardware-software co-design for datacenter-scale AI systems, analyzing real workloads to drive architecture, performance, and cost improvements; prototype and productionize optimizations across accelerators, runtimes, compilers, interconnects, and distributed training/inference; mentor engineers and influence hardware and platform roadmaps.

Design, develop, and operate monitoring and observability solutions for flagship supercomputers. Build data pipelines to process large telemetry and logs, respond to incidents, implement mitigations, write postmortems, and improve reliability, performance, and operational runbooks at scale.

Join Azure PostgreSQL to design, implement, and test features in a relational database engine (query processing, logging, storage, IO, scale-out). Collaborate with stakeholders, write maintainable code, define test plans, operate live services (on-call), and contribute to open-source PostgreSQL and Linux.

Lead architecture and roadmap for Azure PostgreSQL systems; design and review scalable, maintainable database engine features (query processing, storage, transaction logging, recovery, versioning, IO, scale-out). Influence coding standards and run code reviews, write proof-of-concept or production code, contribute to open-source PostgreSQL/Linux, and participate in rotational on-call operations.

Design, build, and manage low-latency, high-performance networking infrastructure for large-scale AI training in Azure. Test and validate devices and firmware, automate deployments, monitor telemetry, troubleshoot live site incidents, participate in on-call duties, and guide datacenter staff on secure hardware operations.

Lead data science strategy across complex customer engagements, turn ambiguous business problems into repeatable ML solutions, define modeling and evaluation standards, drive data readiness and MLOps practices, mentor teams, produce reusable IP, and represent Microsoft in executive and thought-leadership forums.

Design, implement, and optimize large-scale distributed training infrastructure and telemetry for GPU clusters. Profile, benchmark, and debug performance across compute, memory, networking, and storage. Improve collective communication libraries and collaborate with ML researchers and hardware teams to scale models, optimize accelerators, and deliver fleet-wide efficiency improvements and automated recommendations.

Design and develop high-volume, low-latency telemetry and event pipelines for Azure HPC & AI supercomputers. Analyze and improve pipeline fidelity and latency, enable diagnostics for job reliability, partner across teams to deploy end-to-end solutions, lead incident resolution, root-cause analysis, and drive engineering improvements tied to customer outcomes.