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

Design, develop, and optimize high-performance, low-latency networking infrastructure for large-scale AI training on Azure. Benchmark, debug, and improve scalability, reliability, observability; author design documents; act as DRI and participate in on-call operations.

Design, implement, and optimize low-level software for Azure HPC/AI VMs. Diagnose HW/SW integration and performance issues, drive cross-team solutions, maintain service stability, and develop operational playbooks.

Design and implement next-generation networking infrastructure for large-scale AI training on Azure. Lead architecture, write secure high-performance code, create test strategies, perform and coach code reviews, integrate AI tools in the SDLC, and collaborate cross-team to ensure scalability, reliability, observability, and security for distributed HPC/AI systems.

Design, build, and optimize high-performance, low-latency networking infrastructure for large-scale AI training on Azure. Benchmark and improve scalability, reliability, and observability; debug complex network issues; author design documents; act as DRI and participate in on-call operations to restore and maintain service availability.

Design and build monitoring, telemetry, and data pipelines to operate flagship supercomputers at scale. Diagnose and troubleshoot GPU hardware, networking, datacenter and software stack issues, implement systemic mitigations, improve observability, and drive reliability and performance improvements through incident reviews and tooling.

Design, curate, and evaluate large-scale text datasets for LLM pretraining. Build scalable ingestion, preprocessing, filtering, annotation pipelines; run data ablations and analyses; create auditing, visualization, and versioning tools; and collaborate with Safety, Ethics, and Governance to ensure dataset quality, privacy, and responsible AI alignment.

Lead infrastructure analytics for compute, storage, and networking: design scalable data pipelines, define core metrics, build dashboards and APIs, ensure data quality and governance, drive instrumentation fixes, and advise executives on capacity, utilization, and readiness.

Lead end-to-end data science and AI engagements: design, develop, deploy and evaluate models, mentor teams, partner with stakeholders, drive adoption, and apply Responsible AI to deliver measurable business impact.

Lead a team of SREs to operate large-scale hybrid CPU/GPU AI training and inference infrastructure. Design observability, automation, deployments, incident management, security/compliance, and collaborate with ML teams to accelerate research-to-production workflows.

Operate and improve large-scale HPC GPU clusters for AI model training and inference. Build observability, automation, CI/CD, and incident response tooling; lead on-call rotations, ensure security/compliance, collaborate with ML engineers, and optimize capacity and costs for production ML workloads.