Jobs at NVIDIA

Leads NVIDIA’s internal S3-compatible distributed object storage platform team, owning roadmap execution, software development, production operations, reliability, scalability, and service delivery at exabyte scale. Responsibilities include managing and developing engineering talent, driving SLOs, capacity planning, incident response, CI/CD, observability, customer collaboration, and AI-assisted development adoption. The role requires deep distributed storage expertise and hands-on experience operating cloud storage services for large-scale AI infrastructure.

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.

Develop and debug UEFI firmware for NVIDIA gaming consoles, servers, and AI devices. Responsibilities include firmware feature development, architecture documentation, simulation and FPGA bring-up, cross-team issue resolution, and pre- and post-silicon verification. The role requires strong C programming, debugging, operating-system internals, ARM technologies, kernel modules, and device-driver experience, with UEFI, ACPI, SMBIOS, and Linux kernel expertise preferred.

Develop algorithms and high-performance systems across the GenAI lifecycle, including multimodal data preparation, distributed foundation-model training, reinforcement-learning post-training, inference optimization, and open-source AI infrastructure. The role involves improving scalability, efficiency, deployment cost, and model support while collaborating with research, product, and infrastructure teams.

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.

Develop and verify CPU SoC boot firmware for pre- and post-silicon platforms. Define and decompose functional and safety requirements, perform failure analysis (FMEA/DFA), implement secure boot features, collaborate across teams, and support silicon validation and productization.

Build and lead development of NVIDIA’s open-source NeMo Core Platform for executing, evaluating, deploying, and optimizing agentic AI systems across local, Docker, Kubernetes, Slurm, on-premises, and air-gapped environments. Design platform APIs, plugins, SDKs, job orchestration, authentication, RBAC, observability, testing, and reliability features. Provide technical leadership through architecture and code reviews, mentoring, and ownership of complex cross-component problems.

Develop and lead production software-defined networking for NVIDIA’s global GPU cloud infrastructure. Design multi-tenant control and data planes using Open vSwitch, OVN, OpenFlow, Kubernetes, C, and Go. Build networking services, plugins, automation, testing, observability, and reliability improvements. Lead architecture and code reviews, mentor engineers, support on-call operations, and resolve complex production incidents through diagnostics and source-level debugging.

Design next-generation GPU-accelerated computing systems and architectures for cloud, data center, deep learning, analytics, and high-performance computing applications. Develop analytical models, simulators, and visualization tools to evaluate performance, cost, and efficiency trade-offs. Analyze hardware-software interactions in large-scale deployments and collaborate with software, product, and business teams to guide GPU cloud computing strategy.

Verify ASIC inference accelerator designs at block and SoC levels using advanced methodologies, random stimulus, functional coverage, assertions, and UVM. Develop verification infrastructure, debug designs, optimize automated flows, and collaborate with architects, designers, and pre- and post-silicon teams. Apply knowledge of ASIC design flow, power verification, DFT, netlist verification, and machine learning techniques to improve verification processes.

Own and evolve Kubernetes networking for GPU superclusters spanning multiple clouds and thousands of nodes. Design and operate CNI data planes, overlay meshes, VPNs, gateways, load balancers, and tunnels. Identify scale limitations, build network validation environments, diagnose distributed networking failures, and partner with cloud providers on topology. Provide technical leadership while using strong Linux networking, systems programming, and large-scale debugging expertise.

Develop scalable distributed storage management systems for NVIDIA’s large-scale engineering infrastructure. Collaborate with hardware and software teams to define features, optimize workflows, improve software design and testing, and deliver reliable storage solutions. The role requires Linux, storage systems, networked distributed systems, Perl, and Python expertise, with responsibility for architectural decisions balancing cost, performance, reliability, and scalability.

Build and operate security controls, telemetry, detections, and enforcement for NVIDIA’s large-scale EDA compute, storage, and networking infrastructure across on-premises and multi-cloud environments. Investigate threats, respond to incidents, automate secure configurations and compliance evidence, threat-model infrastructure, and manage security technology lifecycles. The role requires hands-on Linux, networking, programming, infrastructure security, and collaboration with infrastructure and security teams.

Builds secure AI and agentic cybersecurity tooling, transforming research prototypes into production-ready services. Responsibilities include developing backend services, APIs, CLI tools, automation, and integration patterns; defining MVPs for ambiguous security and identity challenges; collaborating across research, product, enterprise security, and platform teams; and applying offensive security and adversarial thinking to address AI-specific risks such as injection, tool misuse, unauthorized access, data exfiltration, and unsafe delegation.

Develop and maintain upstream Linux kernel support for NVIDIA GPU virtualization. Build virtualization features in Rust and C, contribute patch series to the Linux kernel, and work across KVM, QEMU, VFIO, PCIe SR-IOV, memory management, and device lifecycle systems. Debug complex host, hypervisor, driver, firmware, performance, and reliability issues while collaborating with maintainers, partners, and distributed engineering teams.

Leads engineering enablement for large-scale AI/HPC clusters, managing a support team, training and documentation, request intake and prioritization, specialist debugging coordination, and user community development. Partners across architecture, software, firmware, networking, storage, datacenter operations, performance, and program teams. Uses service metrics and user feedback to improve workflows, self-service guidance, resource allocation, and support quality.

Conduct novel research in AI security and cybersecurity, focusing on securing AI models, agents, systems, and infrastructure. Develop tools and processes to identify and remediate vulnerabilities, investigate adversarial machine learning, publish findings, and collaborate across NVIDIA. Present complex technical results to security, machine learning, and business audiences while contributing to secure software development and AI safety.

Design and maintain software that models data center power, total cost of ownership, and performance trade-offs across hardware, software, workloads, and deployments. Contribute to architecture, prototyping, testing, regression coverage, debugging, code reviews, and cross-functional interface design. Help resolve complex technical problems, improve engineering practices, and support teammates in adopting effective technologies and processes.

Design and deploy AI networking platforms in customer data centers. Partner with customers on GPU, compute, storage, and fabric architecture; support infrastructure bring-up; troubleshoot InfiniBand, RoCE, Linux, NIC, and switching issues; conduct technical reviews; build proof-of-concepts; and translate customer requirements into product and engineering feedback. Collaborate with systems engineering, product management, and sales while supporting large-scale AI cluster deployments.

Lead the architecture and implementation of NVIDIA’s large-scale data retrieval platform for foundation-model training. Build web crawling, source discovery, indexing, scheduling, fetching, ingestion, provenance, and policy-enforcement systems. Ensure production reliability, scalability, observability, and cost efficiency while supporting open-web, licensed, partner, code, STEM, and multimodal data. Partner across engineering, research, product, legal, privacy, and governance teams, mentor engineers, and set technical direction.