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

Lead architecture and delivery of DGX Cloud full-stack integration across hardware, firmware, OS, and virtualization. Define control and data plane systems for scalable, metered bare-metal and virtualized accelerated computing across varied datacenter environments. Drive technical strategy, stakeholder engagement, cross-functional execution, and lifecycle management from design through operations.

Lead alignment of NVIDIAcompiler codebases with open-source projects, build tooling and workflows to improve developer productivity, automate rebasing/patch management and apply AI-assisted workflows, while partnering with architecture and CI teams to scale changes across NVIDIA.

Architect and demonstrate simulation workflows using DSX Sim and Omniverse libraries to help partners and customers build, validate, optimize, and operationalize AI factory digital twins. Drive integration of Omniverse libraries, develop reference architectures, prototypes, benchmarks, and sample workflows, and coordinate with sales, product engineering, and partners to resolve blockers and advance adoption.

Create and deliver developer-focused technical content and workshops that teach CUDA and NVIDIA GPU technologies. Evaluate new CUDA features, produce tutorials and blog posts, capture developer feedback to inform product decisions, and collaborate across product, engineering, and marketing teams to drive developer adoption.

Advise ISVs on designing, deploying, and optimizing large-scale accelerated AI infrastructure. Lead architecture reviews, POCs, benchmarks, and production deployment guidance across compute, networking, storage, containers, orchestration, observability, and CI/CD. Create reference architectures, sizing guidance, technical playbooks, demos, and whitepapers. Support cluster monitoring, reliability, and performance improvements. Up to 20% travel for customer engagements.

Partner with industry and account teams to design and deliver NVIDIA-based AI, HPC, and GPU/networking solutions for industrial robotics. Engage customers and partners, produce technical documentation and samples, support deployments both in datacenters and at the edge, and lead technical engagements with lighthouse customers and solution partners.

Serve as a customer-facing solutions architect for CAE/CFD/FEA ISVs and OEMs, accelerating simulation and digital-twin workflows on NVIDIA platforms. Advise on GPU-acceleration, physics-informed ML/surrogate modeling, Omniverse integration, architecture analysis, and deliver trainings, demos, and prototypes to improve performance and simulation cycles.

Lead technical engagements to integrate NVIDIA GPU, networking, and AI platforms into industrial robotics solutions. Partner with customers, partners, and internal teams to design, document, and deliver scalable AI/HPC datacenter and edge architectures, provide technical guidance, and create samples, whitepapers, and demos.

Design and scale Kubernetes-native, GPU-accelerated cloud architectures for large-scale robotics simulation, synthetic data pipelines, training, and inference. Advise partners, optimize scheduling, storage, networking, and GPU utilization, and drive adoption of NVIDIA Physical AI frameworks while collaborating with product and engineering teams.

Develop algorithms and optimizations for inference and compiler stack, enhance performance metrics, collaborate with hardware teams, and publish work on novel approaches.