Jobs at NVIDIA

Leads the development and production deployment of deep learning perception systems for autonomous vehicles. Manages engineering managers, researchers, and software engineers; directs architectures for multi-sensor fusion and real-time 3D world modeling; oversees the machine learning lifecycle from data generation through NVIDIA DRIVE deployment; and partners across planning, mapping, hardware, and safety teams while ensuring automotive quality standards.

Develop and optimize GPU-accelerated deep learning inference software for LLMs, multimodal, and generative AI models. Contribute features to vLLM, SGLang, FlashInfer, and NVIDIA inference libraries; implement algorithms, tune performance across GPU architectures, and improve large-scale model serving. The role requires strong C/C++ software engineering, collaboration across framework teams, and expertise in profiling, debugging, performance modeling, GPU programming, or multi-GPU communication.

Contribute to power estimation models for GPUs, explore architecture for energy efficiency, analyze performance versus power, and employ machine learning for modeling power and performance metrics.

Conduct FEA modeling and simulation to evaluate stress, warpage, thermo-mechanical behavior, materials, and reliability in advanced semiconductor packages. Develop modeling methodologies, assess packaging technologies and process options, interpret results, mentor collaborators, and guide material selection, package builds, and manufacturing processes. Collaborate cross-functionally, document methods, and present findings.

Conducts applied research in quantum error correction and fault-tolerant quantum computing. Develops novel decoding methods, logical operations, scalable decoders, and hardware-aware implementations. Collaborates with product, engineering, applied research teams, and external partners to integrate quantum technologies into fault-tolerant software and hardware stacks.

Lead manufacturing support for data center AI accelerator boards, coordinate engineering, diagnostics, supply chain, and factory handover for NPI to mass production. Drive risk mitigation, process improvements, customer qualifications, budget tracking, and cross-functional alignment to ensure production ramp and issue resolution.

Develop, validate, and productize sensor calibration and egomotion algorithms for autonomous vehicles using classical and deep learning methods. Implement high-performance C++/CUDA code, build fleet monitoring tools, run in-vehicle tests, triage calibration issues, and collaborate with OEM partners to deploy calibration features across vehicle variants.

Provides technical enablement and onsite/offsite support for NVIDIA enterprise datacenter hardware. Collaborates with OEM customers through design, validation, production, bring-up, and debugging; reviews server architecture, thermal, mechanical, electrical, schematic, layout, and signal-integrity designs; resolves hardware, firmware, software, PCIe, GPU interconnect, and management issues; develops design guides and technical collateral; and tracks and reproduces bugs.

Leads NVIDIA’s enterprise and AI Technical Program Management organization, including team scaling, portfolio strategy, cross-functional program delivery, governance, executive reporting, change management, operational excellence, and vendor partnerships. The role drives AI-first program operations, automation, cloud and data platform initiatives, risk management, adoption, and enterprise technology transformation while partnering with senior engineering, IT, product, business, and executive stakeholders.

Lead co-design and build of production agentic and Generative AI systems with strategic ISV partners. Define objectives, prototype and ship RAG, multi-agent, long-horizon workflows on NVIDIA's stack, drive adoption, collaborate with Product/Engineering/Research, and influence product roadmaps. Ensure production inference, evaluation, observability, and enterprise deployment (MLOps/Kubernetes/Docker) with strong technical execution and partner-facing leadership.

Lead product-security strategy and delivery for NVIDIA Jetson, L4T, and JetPack across architecture, firmware, Linux, platform software, releases, vulnerability response, and customer deployments. Establish secure-by-design standards, conduct threat modeling and security architecture reviews, coordinate vulnerability remediation, support cybersecurity regulatory readiness, and develop security documentation such as SBOMs and white papers. Influence engineering, PSIRT, legal, compliance, customers, and partners while tracking security and release-readiness metrics.

Design and architect GPU and CPU clock domains for ASICs, balancing functional, physical, testing, power, performance, and area requirements. Develop clocking RTL, collaborate with front-end, physical design, timing, verification, DFT, and software teams, and support synthesis, signoff, silicon debugging, and bring-up. The role also involves optimizing clocking topologies and resolving silicon or programming issues.

Defines and drives system-level diagnostics, verification, testability, and post-silicon validation for complex GPUs, CPUs, SoCs, and AI accelerators. Partners with silicon, software, board, manufacturing, and quality teams to develop production test coverage, debug infrastructure, and diagnostic workflows. Performs hands-on silicon bring-up, lab debugging, data analysis, performance and yield optimization, test correlation, root-cause analysis, and process improvement.

Manage strategic sales relationships with AI-native startups, drive quota attainment, qualify and close complex technology opportunities, maintain forecasts, and develop partnerships with cloud providers, system integrators, ISVs, and OEMs. The role requires positioning NVIDIA CUDA-X solutions, supporting technical and business collaborations, leading cross-functional initiatives, and expanding revenue within the startup ecosystem.

Leads silicon product-definition analysis across performance, power, thermal behavior, yield, and quality. Uses large datasets, statistical models, automation, and visualization to define product configurations and resolve silicon and system constraints. Partners across architecture, design, firmware, validation, manufacturing, and operations from bring-up through production, including BIOS-level voltage-frequency settings. Translates complex technical findings into product decisions, optimization plans, and executive-ready risk assessments.

Lead technical engagements with strategic ISV partners to design, prototype, and ship production agentic and generative AI systems. Drive adoption of NVIDIA stack (NeMo, TensorRT-LLM, etc.), set account objectives and timelines, coordinate cross-functional teams, and feed partner insights into Product, Engineering, and Research to shape roadmaps and deliver scalable enterprise deployments.

Design ASIC memory-subsystem solutions, including microarchitecture, RTL design, synthesis, and timing analysis. Collaborate with architecture, design verification, formal verification, and physical design teams to deliver high-performance SoC interconnects, ordering controllers, and memory accelerators. The role requires proficiency in Verilog or VHDL and Perl scripting, with exposure to coherent interconnects, clock domains, asynchronous interfaces, and CHI, CXL, or PCIe specifications preferred.

Own end-to-end SoC technical programs from architecture through tape-out, bring-up, validation, and productization. Drive schedules, dependencies, technical trade-offs, cross-functional alignment, risk mitigation, and silicon hardware logistics across engineering, operations, software, systems, and marketing. Improve execution processes and evaluate AI-powered tools for program management, logistics, documentation, and engineering productivity. The role requires onsite teamwork in Santa Clara at least three days per week.

Owns enterprise work-management solutions from discovery through deployment and continuous improvement. Leads requirements gathering, prototyping, configuration, testing, adoption, governance, and platform reliability. Configures Jira or comparable platforms, workflows, integrations, dashboards, and permissions; applies automation, scripting, SQL, AI, data privacy, and responsible-AI practices. Partners with business and technical teams to resolve complex issues, support migrations and upgrades, and deliver measurable business outcomes.

Build and lead scalable data platforms, ETL frameworks, streaming pipelines, data lakes, warehouses, and transformation tools. Develop Python- and Spark-based systems for extracting, validating, transforming, and analyzing semiconductor testing data. Support self-service machine learning and analytics platforms, real-time processing, custom visualizations, and complex data ecosystems. Collaborate with IT and business teams to define data strategies, gather requirements, plan projects, and deliver production-ready solutions.