Jobs at NVIDIA

Leads multiple concurrent quantum computing R&D programs, translating research roadmaps into measurable execution plans and scalable technologies. Partners across applied research, engineering, product teams, and external organizations to prioritize work, manage risks, solve technical challenges, and advance quantum technologies toward fault-tolerant computing. Requires strong technical understanding of quantum hardware and software, exceptional communication, and extensive experience managing complex cross-functional technology initiatives.

Lead large-scale DGX Cloud infrastructure programs, coordinating NVIDIA engineering teams with cloud service and NVIDIA cloud providers. Define compute, storage, networking, GPU capacity, and operational requirements; build roadmaps; manage risks, KPIs, lifecycle processes, and partner engagements. Drive infrastructure bring-up, deployment, availability, maintenance, security, compliance, observability, and continuous improvement across global AI cloud capacity initiatives.

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.

Design and optimize agentic AI systems for NVIDIA’s CUDA ecosystem, including agent architectures, multi-agent workflows, tool use, memory, orchestration, training, verification, reward, and evaluation systems. Co-design AI systems across models, inference, compilers, runtimes, libraries, kernels, and GPUs. The role requires strong C/C++, Rust, and Python skills, GPU programming expertise, and experience with agent systems, reinforcement learning, or modern AI inference.

Supports and root-causes complex customer issues involving NVIDIA networking and AI cluster technologies. Develops bug fixes, workarounds, diagnostic tools, and performance-tuning recommendations in collaboration with R&D and support teams. Works with InfiniBand, NVLink, Spectrum-X, and high-performance lossless networks, analyzing logs, traces, metrics, and source code. Provides customer support and may work occasional weekends or holidays.

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.

Develop and debug cloud-native solutions for AI/ML datacenters, focusing on Kubernetes and Slurm enhancements and customer workflows.

Develop and guide sensor fusion functionality for autonomous driving by creating 3D world models and ensuring accurate representations using various signals. Lead technology approaches and mentor team members while adhering to automotive quality standards.

Design, develop, test, and optimize system software for next-generation ARM-based SoCs across pre- and post-silicon phases. Analyze architectural bottlenecks, workload performance, and performance-per-watt tradeoffs; develop hardware/software policies and optimization strategies. Lead performance analysis, competitive benchmarking, architecture documentation, and adoption of improvements across NVIDIA software products and open-source SDKs.

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.

Define system and technical requirements for fault-tolerant quantum computing across hardware, software, and firmware. Develop real-time quantum error correction and calibration approaches, perform systems analysis across qubit modalities, and create and review test cases. Collaborate with product, research, engineering, and external partner teams to advance quantum control systems, quantum computing products, and related NVIDIA technologies.

The Senior LLVM Compiler Engineer will contribute to upstream LLVM development, lead design discussions, and ensure high-quality compiler solutions in collaboration with open-source communities.

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.

Manage and lead a software engineering team building NVIDIA DGX Cloud Kubernetes platforms. Responsibilities include gathering requirements, guiding engineers, developing cloud-based solutions, presenting roadmaps and demos, participating in open-source communities, and partnering across NVIDIA organizations. The role requires expertise in distributed systems, systems programming, Kubernetes, Linux internals, networking, storage, DevOps tooling, container orchestration, APIs, and identity and access management.

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.

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.

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.

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.

Lead and grow a technical team responsible for verifying AI compilers for next-generation workloads. Define formal verification requirements, drive AI-assisted and compiler-aware verification (fuzzing, differential testing, symbolic reasoning, formal methods), and partner with compiler, CUDA, runtime, and ML framework teams to build scalable verification infrastructure and improve production confidence.

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.