Jobs at NVIDIA

Develop and optimize resilient, large-scale cloud services supporting AI training workloads. Design Kubernetes-orchestrated microservices, APIs, job submission abstractions, lifecycle automation, checkpointing, telemetry, and failure recovery systems. Collaborate on distributed systems spanning cloud and on-premises AI clusters, integrating with NVIDIA resiliency infrastructure and HPC platforms. The role requires backend development experience, cloud and container expertise, and familiarity with AI frameworks, schedulers, GPUs, and high-performance computing.

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.

Build, validate, deploy, tune, monitor, and retire high-confidence security detections across SIEM, endpoint, cloud, identity, SaaS, developer, and AI environments. Translate incidents, threat intelligence, hunts, and red-team findings into tested detection logic. Improve detection-as-code workflows, telemetry quality, coverage tracking, and alert context while reducing false positives. Partner with responders and security teams, conduct reviews, document operational content, and coach analysts and engineers.

Drive monthly financial close, forecasting, annual planning, and management reporting for Cloud Finance. Build financial models and scenario analyses, identify cost-saving opportunities, communicate insights to executives, automate finance processes, and analyze large datasets to develop metrics for Cloud and AI products. Partner closely with engineering, procurement, finance, governance, and go-to-market teams.

Architects and builds scalable agentic AI systems spanning runtimes, inference, evaluation, orchestration, distributed execution, ETL, HPC, cloud, Kubernetes, and GPU environments. The role optimizes reliability, performance, scalability, resource utilization, and developer experience; prototypes emerging technologies; writes production software; and provides technical leadership across research, engineering, product, and infrastructure teams.

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.

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.

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.

Research and develop AI-native numerical algorithms that combine machine learning with scientific and engineering solvers. Build solver-in-the-loop pipelines, learned preconditioners, AI-guided multigrid methods, differentiable solver components, and hybrid ML/numerical methods. Evaluate convergence, stability, accuracy, robustness, failures, resource use, and performance. Collaborate across NVIDIA Research, GPU computing, simulation, engineering, semiconductor, and digital twin teams while shaping the applied research agenda.

Leads NVIDIA’s global partner marketing strategy with cloud service providers, driving adoption of accelerated computing platforms. Oversees joint marketing plans, integrated campaigns, events, executive relationships, Market Development Fund budgets, performance reporting, and cross-functional collaboration across product, sales, communications, and partner teams. The role requires extensive high-tech marketing experience, global program leadership, team management, financial analysis, and executive communication.

Design and implement compiler transformations, distributed runtimes, pipeline parallelism, sharding, and communication-computation overlap for large-scale deep learning training on GPU clusters. Develop JAX and XLA integrations, optimize compiler and runtime performance, support open-source JaxPP, collaborate with framework and hardware teams, and debug large training workloads.

Lead technical developer relations and partnership programs for NVIDIA’s global systems integrators and technology partners. Drive adoption of NVIDIA AI and computing platforms through technical enablement, demonstrations, integrations, workshops, and ecosystem development. Advise partner engineering teams, address integration challenges, influence product roadmaps, and collaborate with engineering, product, marketing, sales, and legal teams. Focus areas include generative AI, physical AI, agentic systems, GPU acceleration, cloud computing, and vertical-specific AI solutions.

Lead NVIDIA’s AI for Quantum product strategy, roadmap, and go-to-market initiatives. Translate AI and quantum research into scalable platforms, developer tools, APIs, libraries, and workflows. Partner with researchers, engineers, quantum hardware companies, institutions, and developers to identify applications across quantum design, control, calibration, error correction, and algorithms. Evangelize the platform through technical communications, identify new AI, HPC, and quantum opportunities, and present product strategy to executives.

Designs and builds intelligent compiler systems, compiler-oriented agents, code transformation workflows, and numerical verification infrastructure. The role focuses on low-level IR, code generation, optimization, GPU performance, differential testing, correctness validation, and integration across compiler, runtime, library, hardware, and CUDA teams. Candidates should have strong compiler or verification experience, C/C++ and Python skills, GPU programming expertise, and familiarity with frameworks such as LLVM, MLIR, PTX, or Triton.

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.

Lead the design and implementation of a production GPU-accelerated ray-tracing engine for real-time radio propagation, wireless network simulation, radio maps, and per-link channel updates. Develop GPU algorithms, spatial data structures, multi-GPU scaling strategies, and adaptive temporal-coherence techniques for 5G and 6G RAN applications. Apply electromagnetic propagation knowledge to validate reflection, transmission, diffraction, and scattering models.

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.

Leads technical engagements with Tier-1 telecommunications customers and partners, mapping workloads to NVIDIA technologies. Designs scalable architectures, integration milestones, and deployment plans; validates solutions; troubleshoots complex issues; enables platform adoption; and communicates customer needs to engineering and product teams. The role requires deep telecom stack expertise, customer-facing technical leadership, and experience with software, hardware, AI inference, networking, containers, Kubernetes, and potentially CUDA/GPU programming.