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

Design, develop, test, and optimize system software for next-generation ARM-based SoCs across pre- and post-silicon phases. Analyze architectural bottlenecks, workload performance, and performance-per-watt tradeoffs; develop hardware/software policies and optimization strategies. Lead performance analysis, competitive benchmarking, architecture documentation, and adoption of improvements across NVIDIA software products and open-source SDKs.

Build and maintain automation flows and methodologies for VLSI interposer design. Develop tools and applications (Python/C++) to construct, analyze, and validate interposer designs, improve designer workflows, and integrate AI/ML-assisted techniques into EDA environments.

Lead research, architecture, and production development of hardware formal verification tools enhanced by agentic AI. Build formal verification algorithms, multi-agent systems, solver orchestration, RTL analysis, automated SystemVerilog Assertion generation, and high-performance C/C++ implementations for commercial EDA suites. The role requires deep expertise in formal methods, hardware verification, AI frameworks, RTL languages, and 15+ years developing advanced EDA tools.

Define system and technical requirements for fault-tolerant quantum computing across hardware, software, and firmware. Develop real-time quantum error correction and calibration approaches, perform systems analysis across qubit modalities, and create and review test cases. Collaborate with product, research, engineering, and external partner teams to advance quantum control systems, quantum computing products, and related NVIDIA technologies.

Develop optimized GPU algorithms and high-performance machine learning solutions for vector search, databases, and data processing. Responsibilities include designing, implementing, benchmarking, debugging, and optimizing C++, CUDA, and Python libraries; developing clustering and visualization algorithms; collaborating cross-functionally; and contributing to open-source projects such as cuML, cuVS, and RAFT.

Research and develop GPU-acceleration techniques for high-performance databases, ETL, and data analytics applications. Analyze data-intensive workloads, optimize algorithms, memory management, and CUDA kernels, and investigate CPU/GPU bottlenecks. Collaborate with research, hardware, software, and industry teams to influence next-generation architectures, programming models, and data-processing solutions.

Design, develop, test, and extend NVIDIA’s large-scale object storage platform. Responsibilities include improving availability, reliability, durability, fault tolerance, and performance across deployments exceeding 10,000 nodes and exabytes of data. The role also automates storage infrastructure provisioning, management, and monitoring; supports AI/ML research teams; owns product delivery through support; and contributes to open-source storage projects.

Drive NVIDIA platform adoption across enterprise AI and semiconductor ecosystems. Develop and execute go-to-market strategies, build strategic partnerships, enable sales teams, create and close complex opportunities, grow revenue and pipeline, provide product feedback, and lead event strategy. The role requires deep AI/ML expertise, executive relationship management, solution architecture, business strategy, and up to 25% national travel.

The Senior LLVM Compiler Engineer will contribute to upstream LLVM development, lead design discussions, and ensure high-quality compiler solutions in collaboration with open-source communities.

Develop and implement core firmware features for Mellanox NICs supporting cloud, HPC, and storage networking. Collaborate with architecture and software teams, create verification tests, debug complex hardware/software systems, and contribute to firmware maintenance, qualification, and releases. The role requires strong C/C++ development, networking knowledge, PCIe and virtualization experience, scripting skills, and familiarity with distributed, high-performance computing environments.