Jobs at NVIDIA

Develop Linux kernel and user-space power and thermal management software for NVIDIA Tegra and embedded platforms. Optimize performance, efficiency, latency, compute and memory utilization for edge AI and robotics workloads. Collaborate with hardware, software, and power architecture teams on platform estimation and optimization. Requires strong C, Linux, embedded systems, ARM, and system-level debugging experience.

Leads an engineering team building NVIDIA’s large-scale AI infrastructure platform. Owns roadmap, architecture, reliability, on-call health, Kubernetes-based systems, scheduling, data movement, and fleet-scale automation. Partners with research, infrastructure, cloud, storage, networking, vendors, and open-source communities to turn AI training needs into reliable platform capabilities. Hires and develops senior engineers, drives technical decisions, and improves service reliability through observability, incident response, and postmortems.

Own network infrastructure reliability and availability, automate operational processes, monitor performance, resolve production issues, and drive continuous improvements. Partner with architecture and deployment teams to ensure supportable implementations, establish operational standards, document procedures, conduct root-cause analyses and blameless postmortems, and achieve service-level objectives. The role also mentors engineers and leads significant network operations projects across campus and data center environments.

Manage a multidisciplinary engineering team responsible for GSP firmware reliability, resiliency, field debugging, pre-silicon readiness, release execution, validation, and quality metrics. Partner with technical leaders on architecture, prioritization, and delivery while driving incident response, blocker resolution, test coverage, and robust GPU driver software releases across product lines.

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.

Develop hardware diagnostics and resiliency models for NVIDIA GPUs, SoCs, CPUs, and networking systems. Analyze hardware failures, RAS metrics, FIT, availability, fault attribution, containment, and recovery. Build automation for diagnostic jobs across compute clusters, create analytics dashboards, and improve telemetry-based failure classification. Collaborate with engineering teams to enhance diagnostic coverage, reliability, and resiliency for AI datacenter and edge-computing workloads.

Optimize real-time autonomous driving context-fusion software integrating perception, map, and localization data. Profile pipelines, identify bottlenecks, and improve CPU utilization, memory consumption, latency, architecture, and data flows on automotive platforms. Develop benchmarks and regression tests, preserve algorithm correctness and output quality, and collaborate with perception, mapping, localization, platform, and algorithm teams on reliable production software.

Leads NVIDIA’s strategic UX research program for internal IT and employee productivity products. Conducts mixed-methods research using interviews, contextual inquiry, diary studies, surveys, support data, and product analytics. Establishes research standards, participant and insights infrastructure, and adoption metrics; translates findings into product and investment decisions; and coaches product managers and designers. Focus areas include AI tool adoption, workplace technology, employee experience, and knowledge-worker productivity.

Lead technical engagement for AI training and infrastructure at scale. Collaborate with engineering teams and customers to ensure successful deployment and optimization of AI/ML solutions.

Develop and execute go-to-market strategies for NVIDIA’s networking products, coordinating Product, Sales, Marketing, and external partners. Create sales enablement materials, training, launch plans, partner co-marketing assets, communications, and product demonstrations. Translate complex networking technologies into clear positioning for technical and executive audiences, manage sales content repositories, and align cross-functional teams to deliver successful product launches.

Advise research and product teams on AI regulation, transparency, safety, and development standards. Help build and operate global AI compliance programs, draft AI frameworks and transparency disclosures, support regulator and standards-body engagement, and provide legal guidance across AI governance matters. The role requires collaboration with legal, security, research, and product teams, along with strong communication, writing, organization, and project-management skills.

Build and optimize deep learning performance libraries and tools, including Transformer Engine, for efficient training and deployment of large language models. Develop and benchmark models on NVIDIA GPUs, contribute to PyTorch, JAX, and MLPerf, optimize multi-GPU and multi-node systems, engage with open-source communities and enterprise partners, and influence future hardware and platform software designs.

The role involves developing automated tests and tools for NVIDIA software, focusing on improving quality and efficiency through Python programming and AI-assisted development tools.

Develop AI-driven automated tests, tools, utilities, and frameworks for NVIDIA GPU device drivers and SDKs across Windows and Linux. Analyze test results, isolate defects, debug root causes, improve code coverage, validate software across hardware and interface matrices, and support CI/CD testing. The role includes code reviews, workflow optimization, task prioritization, reporting, and close collaboration with engineering leadership in a fully onsite environment.

Integrate modular AI data center infrastructure from factory production through site commissioning and operational handover. Coordinate MEP trades, liquid-cooling and power-distribution interfaces, BMS and EPMS controls, commissioning requirements, installation quality, factory and site reviews, deficiency correction, as-built documentation, operator training, and reference architectures. The role requires extensive critical-facility delivery experience, strong systems integration expertise, and collaboration with owners, designers, contractors, OEMs, and commissioning authorities.

Design and architect next-generation GPU system interfaces and scheduling features. Collaborate with software, hardware, and verification teams; develop C++ functional and performance models; create test plans; and support post-silicon validation. The role requires graduate-level education or equivalent experience in computer architecture, computer science, electrical engineering, or a related field, along with strong C or C++ skills.

Lead CPU performance verification strategy, test planning, stimulus and microbenchmark development, regression debugging, coverage closure, and performance correlation tooling. Collaborate with architects and design engineers on CPU microarchitectural features, guide junior engineers, and support validation across CPU designs. Requires CPU microarchitecture knowledge, workload analysis, Perl or Python scripting, C/C++ proficiency, and 12+ years of experience. ARM A64 and x86 experience are preferred.

Drive end-to-end quality in AI microservices, lead testing strategies, mentor engineers, and enhance automation practices while validating AI inference workflows.

Own the roadmap and delivery of AI infrastructure resilience platform features, including telemetry, health checks, attribution, observability APIs, and deployment experiences. Translate developer needs into product strategy, collaborate with engineering on architecture and execution, engage open-source contributors through GitHub, and align product, marketing, field, and engineering teams. The role requires deep technical expertise in AI infrastructure, Kubernetes, developer platforms, or related systems, plus extensive product management or engineering experience.

Designs and delivers large-scale AI and HPC GPU cloud infrastructure for NVIDIA Cloud Partners in the telecommunications sector. Serves as the primary technical advisor across solution design, development, integration, deployment, and production. Builds proofs of concept, integrates AI software stacks and models, leads customer technical engagements, delivers workshops and reference architectures, and partners with engineering, product, sales, and business teams. The role requires expertise in deep learning, LLMs, distributed cloud architectures, GPU technologies, MLOps, and customer-facing solution engineering, with up to 40% travel.