Jobs at NVIDIA

Develop platform software using container runtimes and Kubernetes to enable GPU-accelerated applications. Design systems software for container orchestration, cloud-native environments, and distributed systems. Integrate software across NVIDIA engineering teams while addressing performance, security, reliability, and evolving priorities. The role involves Rust and Go development, Linux system internals, container sandboxing, confidential computing, virtualization, and GPU resource management.

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.

Own NVIDIA’s config-as-code foundation for its EDA compute farm. Design LSF deployment schemas, build federated deployment pipelines with staged rollout and rollback, eliminate configuration drift, and create regression testing for scheduled LSF upgrades. Partner with an LSF specialist to encode scheduler expertise into templates and policies. The role also requires infrastructure automation, GitOps at scale, tooling development in Go, Python, and shell, and management of stateful production infrastructure.

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.

Manage strategic state and local government accounts for NVIDIA’s AI and accelerated computing solutions. Own the full technology sales cycle, develop territory and account plans, build executive and technical relationships, collaborate with solution architects and channel partners, navigate public-sector procurement, forecast revenue, and achieve sales quotas. The role requires consultative selling to government organizations and travel of at least 25%.

Design, develop, verify, and maintain embedded system software and kernel drivers for NVIDIA Tegra ARM-based SoCs. Responsibilities include pre-silicon modeling, silicon bring-up, core OS enablement, hardware and software collaboration, unit testing, documentation, and system-level debugging. The engineer will also influence hardware architecture through design specifications and support next-generation SoC platforms.

Build and manage strategic relationships with R1 universities, research institutions, and government leaders. Drive adoption of NVIDIA’s accelerated computing platform across scientific research communities, identify business growth opportunities, forecast revenue, and execute go-to-market strategies. Engage principal investigators, research labs, IT teams, and internal technical groups to advance applications in AI, life sciences, climate science, engineering, and other scientific domains. Travel 25% or more as needed.

Develop and maintain graphics system software, user-mode drivers, platform code, APIs, and SDKs across GPU architectures and operating systems. Ensure backward and forward compatibility across driver releases, collaborate on graphics and deep learning technologies, and support next-generation GPU design, development, and validation.

Drive technical engagements for NVIDIA’s on-premises AI Factory solutions. Advise partners, OEMs, and enterprise customers; design and validate scalable architectures; support deployments and troubleshooting; enable adoption of NVIDIA platforms; and communicate customer needs to engineering and product teams. The role requires hands-on expertise across AI infrastructure, hardware, firmware, operating systems, cluster management, workload orchestration, and system integration.

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.

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.

Investigate, assess, and remediate vulnerabilities across NVIDIA data center platforms. Responsibilities include vulnerability triage, exploitability analysis, CVSS and CWE assessment, security fixes, regression testing, dependency updates, automation, and coordinated disclosure. The role also improves secure development lifecycle practices through threat modeling, security reviews, testing, and security automation while collaborating across distributed engineering teams.

Leads engineering teams developing platform software for GPU-accelerated applications using Kubernetes, container runtimes, Kata Containers, and Confidential Containers. Responsibilities include enabling GPUs as Kubernetes resources, integrating software across NVIDIA’s technology stack, managing priorities, and delivering secure, reliable distributed systems. The role requires deep systems software expertise, engineering management experience, and hands-on knowledge of containers, Linux, cloud computing, virtualization, and confidential computing technologies.

Develop and maintain automated integration and deployment for components in a Kubernetes-based software stack. Manage third-party dependencies, validate compatibility, maintain integration workflows, troubleshoot cross-component issues, and ensure compatibility with NVIDIA hardware. The role requires extensive Linux software development experience, Python proficiency, Kubernetes and container expertise, and collaboration with QA, CI/CD, development teams, and upstream projects.

Lead performance practices for large-scale AI systems, enhance datacenter products, and resolve complex performance issues through collaboration and technical expertise.

The Senior HPC Architect role involves deploying large-scale GPU compute clusters, optimizing system administration, and collaborating with researchers on HPC and AI solutions.

Leads multifunctional initiatives defining safety architectures and requirements for autonomous vehicles. Develops safety concepts and analysis supporting continuous software delivery, improves safety metrics and analysis tools, evaluates safety data, and communicates readiness results. Collaborates with perception, AI, motion planning, and architecture experts to ensure DRIVE products meet rigorous safety standards.

Deploy, manage, validate, and optimize large-scale AI compute and HPC infrastructure in Linux environments. Serve as a technical liaison among NVIDIA, partners, and enterprise customers; lead planning through implementation, document handoffs, transfer knowledge, troubleshoot hardware and software, and provide feedback for product and architecture improvements. The role involves networking, system design, automation, benchmarking, cluster provisioning, and customer-site travel up to 30%.

As a Principal Software Engineer, you'll develop software systems for NVIDIA's rack-scale infrastructure, defining software architecture, collaborating across teams, mentoring engineers, and ensuring high-quality technical decisions in complex environments.

Design and implement high-performance, scalable storage systems and client libraries for AI workloads. Build data loading, checkpointing, caching, POSIX-style and object-store integrations, observability and telemetry, and validate performance with platform, SRE, and AI teams using modern engineering practices.