Jobs at NVIDIA

Lead security engineering for RTOS, hypervisor, and embedded virtualization: design and review defenses, collaborate on CPU/SoC security, perform threat modeling and automotive TARA, review low-level C/C++ code, build fuzzing and test infrastructure, assess vulnerabilities, and drive security fixes and verification toward automotive safety and security standards.

Lead technical program management for NVIDIA GPU software, including foundational kernels, drivers, and firmware. Align chipset and product roadmaps with software capacity, coordinate cross-functional dependencies, make prioritization decisions, partner with hardware and release planning teams, improve planning systems, and track execution metrics. The role requires extensive software program management experience and offers opportunities to influence global priorities and long-term process improvements.

Design and implement lane-level localization, perception-map association, and lane selection systems using navigation maps. Develop efficient, safe C++ algorithms and productize software to automotive quality and safety standards for deployment in consumer autonomous vehicles. Work with large-scale real and synthetic datasets and collaborate across teams.

Lead technical program management for NVIDIA GPU software, including kernel components, drivers, and firmware. Align chipset and software capacity roadmaps, coordinate cross-functional dependencies, manage priorities, track execution metrics, and improve planning processes. Partner with hardware, release planning, and customers to deliver software products while balancing near-term timelines with long-term capability development.

Lead design, integration, and delivery of SONiC networking features for NVIDIA products. Develop high-quality C++/Python code in an open-source, continuous-deployment environment, collaborate with internal and external teams, manage chassis/networking features, and engage with the SONiC community to drive next-generation switch solutions.

Lead pre-silicon system software bring-up and validation on simulation platforms. Develop automation, tools, and workflows to improve simulation-based development, debug complex cross-stack issues, enable teams to use virtual platforms, and drive platform readiness for new chips in early-development environments.

Provide advanced technical support for NVIDIA InfiniBand, NVLink and GPU-based HPC/AI infrastructure. Troubleshoot installations, networking and interoperability issues on multi-distro Linux, reproduce and resolve customer problems, use packet analysis and cluster tools, and collaborate with engineering, marketing, and support teams to improve processes and documentation.

Develop CUDA compiler front-end capabilities, including language features, diagnostics, semantic analysis, and code generation. Improve compiler feedback for developers and AI agents, track evolving C++ standards, and contribute across LLVM optimization, code generation, and MLIR. Collaborate with compiler, library, and developer tools teams to deliver robust production software.

Lead technical program management for NVIDIA GPU software, including kernel components, drivers, and firmware. Align chipset and product roadmaps with software capacity, coordinate cross-functional dependencies, make prioritization decisions, partner with hardware and customers, improve planning systems, and track execution metrics. The role requires extensive software product delivery experience and supports process improvements across graphics, machine learning, and accelerated computing programs.

Develop and deploy scalable AI solutions, distributed training and inference systems, and MLOps pipelines across cloud and NVIDIA platforms. Serve as a technical lead for customers and partners, optimize large-scale workloads for performance and cost, diagnose full-stack infrastructure problems, and create open-source tools and reference architectures. The role requires strong AI/ML lifecycle experience, systems expertise, programming skills, and communication abilities.

Design and develop RTOS and hypervisor features, bring up and optimize software stacks on NVIDIA Tegra SoCs, and debug system-level issues across hardware, firmware, RTOS, and virtualization layers. The role develops high-integrity automotive software using safety and security practices, while leading cross-team technical initiatives and coordinating execution. Candidates need strong C/C++ systems programming, embedded software expertise, operating-system knowledge, hardware-assisted virtualization experience, and at least eight years of experience.

Lead product strategy and multi-quarter roadmaps for NVIDIA’s AI Factory software-defined networking stack. Guide upstream development across OVN, OVS, and OVN-Kubernetes; define requirements, priorities, and architecture trade-offs; coordinate dependencies across DPU, fabric, virtualization, and engineering teams; and translate partner and operator needs into product releases, reference deployments, and community strategy.

Design and implement GPU compiler components, optimize compiler performance, solve graphics and parallel-computing compilation problems, collaborate with GPU driver and architecture teams, contribute to LLVM and DXC open-source projects, and help define SPIR-V extensions and standards.

Build and operate NVIDIA’s serverless AI inference platform across globally distributed, GPU-backed Kubernetes clusters. Lead feature implementation, testing, performance optimization, reliability improvements, customer issue resolution, reference architecture development, and technology evaluation. Mentor engineering teams on scalable AI/ML workloads and contribute to product requirements and roadmap development.

Leads NVIDIA’s AI Cluster Runtime product, setting direction for a validated, reproducible GPU Kubernetes software stack. Expands support across accelerators, clouds, and AI workloads; oversees version-locked recipes, supply-chain provenance, and reliable deployment. Represents the project to customers, partners, and the open-source community while driving community adoption. Requires deep Kubernetes GPU infrastructure expertise, GitOps configuration delivery experience, and a strong open-source contribution record.

Develop Rust-to-GPU compiler pipelines, custom intermediate representations, optimizers, and ahead-of-time, just-in-time, and link-time compilation tooling for NVIDIA GPUs. Design Rust-based safety abstractions across GPU execution boundaries and expose low-level CUDA hardware features. Responsibilities include compiler architecture, GPU code generation, debugging, profiling, benchmarking, and independently driving complex engineering initiatives from research through production.

Develop and implement core firmware features for Mellanox NICs supporting cloud, HPC, and storage networking. Collaborate with architecture and software teams, create verification tests, debug complex hardware/software systems, and contribute to firmware maintenance, qualification, and releases. The role requires strong C/C++ development, networking knowledge, PCIe and virtualization experience, scripting skills, and familiarity with distributed, high-performance computing environments.

Develop platform software using container runtimes and Kubernetes to enable GPU-accelerated applications. Build GPU resource support for container orchestrators, integrate software across NVIDIA’s technology stack, and contribute to cloud-native systems. The role requires systems programming, Linux internals, distributed systems, cloud computing, and performance, security, and reliability expertise. Responsibilities include solving complex problems, managing multiple priorities, and collaborating across engineering teams.

Leads site reliability operations and Tier 3 enterprise support across identity, messaging, compute, endpoints, databases, networking, and datacenter infrastructure. Owns incidents, SLAs, compliance, vulnerability remediation, asset management, root-cause analysis, automation, and permanent fixes. Provides technical leadership and mentoring, supports executives, coordinates with global platform teams and vendors, leads site projects and major incidents, and represents local priorities in regional and global IT initiatives.

Develop production-grade multimodal AI systems for L3/L4 autonomous vehicles. Build sensor-fusion and spatiotemporal world models combining camera, LiDAR, radar, navigation, and vehicle-state data. Create multitask models for road understanding, occupancy, perception, prediction, and planning; develop scalable training and evaluation pipelines; investigate foundation-model approaches; optimize models for real-time NVIDIA hardware; and collaborate across AV engineering teams to deploy robust systems.