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

Designs and deploys large-scale NVIDIA AI and HPC GPU infrastructure for cloud partners and strategic customers. Leads technical engagements across the customer lifecycle, supports infrastructure development and production, builds networking and compute proofs of concept, troubleshoots performance and scaling issues, and delivers technical presentations and workshops. Partners with sales and business teams to identify opportunities and provides customer feedback to influence product strategy.

Lead NVIDIA’s RL post-training frameworks strategy and engineering ecosystem across distributed training, inference, rollout, evaluation, orchestration, and NVIDIA platforms. Build and manage globally distributed teams, prioritize upstream and internal investments, establish benchmarks and execution metrics, and drive reliable open-source integrations. Partner with research, product, hardware, CUDA, networking, and external communities to scale reinforcement learning workloads across GPUs and heterogeneous systems.

Design, deploy, maintain, tune, and secure enterprise Linux environments supporting high-performance EDA compute farms. Lead infrastructure-as-code automation, configuration management, continuous deployment, and tooling development using Python, Bash, or Go. Manage LDAP/SSSD naming services, NFS and Autofs storage, virtualization, container infrastructure, registries, networking, and infrastructure telemetry. Support highly available, scalable systems and potentially integrate cloud platforms, CI/CD pipelines, EDA toolchains, and HPC clusters.

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.

Design and implement GPU compiler components, optimize compiler performance, solve graphics and parallel-computing compilation problems, collaborate with GPU driver and architecture teams, contribute to LLVM and DXC open-source projects, and help define SPIR-V extensions and standards.

Design and implement LLM-first APIs and documentation for CUDA-X math libraries. Develop tools, metrics, and processes to improve AI-agent code generation and transform library APIs into LLM-friendly formats. Collaborate with stakeholders on LLM-enhanced releases while prototyping solutions for code-generation platforms. Requires expertise in LLMs, fine-tuning, reinforcement learning, RAG, MCP, agent tooling, composable API design, and technical documentation.

Design, deploy, operate, and automate reliable large-scale EDA infrastructure services on NVIDIA hardware. Build tools that reduce manual work, support distributed private and public cloud systems, define service-level objectives, improve observability, and participate in incident response and on-call rotations. The role also provides systems-design guidance to peer teams and may involve Kubernetes, OpenStack, Docker, Slurm, multi-cloud infrastructure, networking, storage, and GPU-based systems.

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.

Develop Rust-to-GPU compiler pipelines, custom intermediate representations, optimizers, and ahead-of-time, just-in-time, and link-time compilation tooling for NVIDIA GPUs. Design Rust-based safety abstractions across GPU execution boundaries and expose low-level CUDA hardware features. Responsibilities include compiler architecture, GPU code generation, debugging, profiling, benchmarking, and independently driving complex engineering initiatives from research through production.

Build and scale robotics simulation and sim-to-real workflows for NVIDIA partners. Develop solutions involving robot rigging, physics, rendering, sensor simulation, synthetic data, GPU computing, and learned policies. Lead technical evaluations, proofs of concept, workshops, and reference implementations; diagnose performance and integration issues; validate hybrid physics and neural approaches; and collaborate with research, engineering, product, and customers to guide platform adoption and roadmaps.