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

Develop and productize machine learning models for autonomous vehicle perception, including LiDAR and camera perception, sensor fusion, detection, classification, segmentation, and tracking. Build scalable data, training, metrics, and performance workflows, and implement efficient C++ code for the NVIDIA DRIVE AV platform. Collaborate across engineering teams, track advances in machine learning, and optimize models for real-time embedded inference.

Owns the segment sales strategy and revenue growth for NVIDIA DOCA and BlueField infrastructure software. Builds pipelines, forecasts, customer and partner plans, and commercialization paths across networking, cybersecurity, storage, cloud infrastructure, and data services. Develops lighthouse customers and ecosystem partnerships, shapes complex pricing and licensing opportunities, leads executive and technical discovery, and enables sellers and partners to communicate measurable customer value. Requires extensive enterprise infrastructure software sales, business development, technical sales, and partner go-to-market experience.

Design and sign off timing for custom digital, semi-custom, and analog mixed-signal circuit macros in deep submicron processor technologies. Responsibilities include transistor-level circuit design, STA, SPICE simulation, timing modeling, power and reliability analysis, timing constraints, ECOs, and timing closure. Collaborate across physical design, clocks, DFT, and other teams while contributing to hardware security, adaptive clocking, power management, and AI/ML-enabled design methodologies through tape-out and validation.

Design, train, optimize, and deploy large-scale deep learning models, including LLMs, VLMs, and VLAs, for end-to-end autonomous driving and robotics. Integrate production ML systems with vehicle firmware, ensuring safety, reliability, and real-time performance. Develop training datasets and collaborate across teams to bring AI models into mass-production vehicles.

Develop GPU-accelerated techniques for generative AI training and inference, optimize algorithms and performance across NVIDIA CPU and GPU architectures, contribute to frameworks, libraries, and open-source projects, and collaborate with developers, researchers, and hardware and software engineering teams. The role focuses on LLMs, diffusion models, multimodal systems, low-level optimization, and communicating innovations through technical publications and industry conferences.

Build and operate automation and tooling for large-scale NVIDIA GPU infrastructure. Responsibilities include bare-metal provisioning, hardware validation, firmware and software upgrades, cluster lifecycle management, BMC and Redfish integration, failure diagnosis, reliability engineering, incident response, and automated repair. The role works across hardware, networking, Linux, Kubernetes, data center operations, and partner teams to safely bring GPU capacity into production.

Own the vision, roadmap, and delivery priorities for an AI infrastructure resilience platform. Partner with engineering, cloud providers, and cluster operators to improve GPU node health, workload failure attribution, scheduler readiness, and developer experience. Lead open-source community engagement, translate operational requirements into product capabilities, and guide architecture and execution. The role requires deep GPU cluster operations expertise, knowledge of hardware and software failure signals, and experience supporting large-scale AI infrastructure.

Lead technical engagements with OEM and deployment partners to design, validate, and deploy scalable NVIDIA AI infrastructure. Map customer workloads to NVIDIA hardware and software, co-design architectures, resolve complex technical issues, drive platform adoption, and influence product priorities. Lead AI Factory build-readiness execution, deployment validation, technical enablement, and issue resolution across large-scale GPU infrastructure deployments. Travel up to 50% is required.

Own and evolve NVIDIA’s internal developer platform for measuring, tracking, and reporting open-source readiness across DSX OS projects. Lead the product roadmap, production reliability, automation, self-service onboarding, scoring, dashboards, and feedback loops. Replace manual reviews with dependable tooling and ensure data integrity. The role requires strong software engineering, production ownership, experience with agentic coding tools, trusted reporting, and end-to-end product leadership.

Design and develop physical layouts for silicon interposers used in NVIDIA products. Collaborate with package technical leads, signal integrity and technology engineers, and design teams to deliver package solutions. Conduct feasibility studies, floorplanning, custom layout, design-rule and netlist verification, and layout trade-off analysis. Resolve manufacturing, electrical, cost, and scheduling issues while using Cadence tools and verification flows such as DRC and LVS.