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

Lead and coach a storage production engineering team to design, deploy, and operate large-scale storage systems. Own capacity planning, lifecycle management, HA/disaster recovery, automation, monitoring, incident response, and partnerships with engineering and AI teams to improve data pipelines and performance.

Lead and grow an engineering team building GPU‑accelerated deep learning inference frameworks. Drive roadmap, collaborate with compiler/libraries/research teams, optimize LLM and multimodal model performance, and oversee multi‑GPU communications and production deployment of inference software.

Design and implement production-grade VLSI/EDA tools and methodologies for multi-die package design. Build flows, applications, and automation to construct, analyze, validate, and optimize package designs to accelerate tape-out and scale engineering teams.

Deploy, manage, validate, and optimize large-scale AI compute and HPC infrastructure in Linux environments. Serve as a technical liaison among NVIDIA, partners, and enterprise customers; lead planning through implementation, document handoffs, transfer knowledge, troubleshoot hardware and software, and provide feedback for product and architecture improvements. The role involves networking, system design, automation, benchmarking, cluster provisioning, and customer-site travel up to 30%.

Design and build RL post-training infrastructure for AI, optimizing training-inference-rollout on distributed systems while ensuring fault tolerance and scalability.

Lead hardware-software co-design for LLM inference: drive and document architecture, build executable C++ hardware models, optimize performance with good algorithms and parallelism, diagnose cross-team chip and subsystem issues, and automate workflows to improve efficiency.

Drive developer enablement for CUDA-Q by creating docs, examples, tooling, MCP servers, Agent Skills, prompt templates, and benchmarks. Analyze developer and agent workflows, define documentation and agent-consumable standards, run evaluations and metrics, and translate research into practical guidance to improve developer time-to-value for quantum and GPU-accelerated applications.

Lead GPU and NVLink-based cluster design and validation for large-scale AI and HPC deployments. Advise cloud partners on architectures, perform performance modeling, debug deployment issues, support NPI rollouts, and relay field feedback to engineering.

Design and implement functional GPU architecture models and simulation platforms. Develop tools, tests, and validation infrastructure, collaborate with architecture and engineering teams, and apply AI/ML to improve simulation and analysis workflows.

Own and optimize IBM Spectrum LSF scheduling across 15–25 federated cells. Diagnose scheduler latency, MultiCluster forwarding and cross-cluster contention, design scalable cell topology, and encode scheduler policy through infrastructure as code. Partner with CAD and methodology teams on large-scale EDA workloads, license constraints, and tape-out bursts. Requires deep LSF internals expertise, production MultiCluster experience, strong Linux fundamentals, and Python, Perl, and shell scripting skills.