Jobs at NVIDIA

Lead end-to-end execution of NVIDIA's Windows Client AI software platform programs. Coordinate cross-functional teams, translate product strategy into engineering plans, manage risks and dependencies, collaborate with partners (Microsoft, OEMs, ISVs), and drive releases for AI frameworks, inference runtimes, model optimization, Windows integration, and developer tools.

Develop and optimize deep learning, graph, and ML techniques for GPU architectures; work with customers to deliver AI solutions; collaborate with architecture, research, libraries, and system software teams.

Design, architect, develop and debug native and para-virtualized Linux/QNX device drivers and Android HALs for NVIDIA DRIVE OS. Troubleshoot system-level issues, lead software architecture discussions, gather requirements, coordinate cross-functional teams, and participate in feasibility, safety evaluations, and product lifecycle activities to integrate DRIVE solutions into automotive platforms.

Develop and integrate automation and UI tools for certification programs (HLK, HDMI, DP, ESXi, Google xTS). Build multi-agent, AI/ML-enhanced solutions, work with certification kits, debug certification issues, improve processes, and coordinate cross-functional teams to ensure certification-level product quality.

Design and optimize on-device AI inference stacks for RTX and DGX systems: develop inference runtimes, optimize models and pipelines for low-latency/efficient memory use, perform system-level debugging and performance-accuracy analysis, and collaborate across software, research, and product teams to productionize local AI workloads.

Design and implement scalable cloud infrastructure and CI services on GitHub and Kubernetes to run millions of automated jobs. Work on job scheduling, resource management, automated recovery, metrics/alerting/storage services, and apply ML to improve performance. Collaborate across teams and time zones to deploy features across thousands of servers.

Lead enterprise customers in deploying Generative AI solutions, architect production systems, and mentor on AI Factory deployments while optimizing MLOps and inference processes.

Lead design, deployment and scaling of on-prem Kubernetes container infrastructure and CI/CD platforms. Build automation, observability, and analytics tooling; maintain OSS CI/CD (GitLab/GitHub/Jenkins); develop integrations, monitor metrics, and support data-center compute environments for Windows and Linux.

Develop platforms and software for Media & Entertainment and Broadcasting, focusing on Kubernetes deployment, networking, and automation within the GPU Software team at NVIDIA.

Design and implement compiler optimizations and features for NVIDIA CPUs, collaborate on hardware–software co-design, analyze AI/HPC workloads for performance improvements, contribute upstream to open source compiler projects, and develop high-performance compiler software targeting data center and supercomputing deployments.

Develop and improve NVIDIA's GPU assembler and disassembler tools, collaborate with GPU architecture and debugger/profiler teams, ensure high-quality code generation and performance for HPC/DL/autonomous workloads, and engage with customers to gather feedback and drive product improvements.

Design, build, and operate a Kubernetes-based platform for global network infrastructure. Own cluster lifecycle, provisioning, upgrades, GitOps delivery, observability, capacity, and recovery. Develop automation, provide production support and on-call incident response for network services, and drive issues from detection through verified resolution.

Build and optimize on-device AI inference stacks for RTX and DGX systems. Design inference runtimes and execution stacks, optimize models and pipelines (quantization, pruning, sparsity), perform system-level debugging and performance-accuracy tradeoffs, and create infrastructure for performance sweeps and production readiness across various ML workloads and hardware.

Lead design, operation, and reliability of large-scale AI/HPC clusters. Manage day-to-day operations, incident response, automation, performance tuning, scheduler and storage optimization, and collaborate with researchers and global teams to improve GPU-accelerated infrastructure.

Lead cross-functional teams at a contract manufacturer to resolve quality events, perform root-cause analysis, control test yield, provide onsite test support and training, and bridge hardware, firmware, and manufacturing for high-volume production.

Lead root-cause and failure analysis for board-level hardware, develop corrective/preventive actions, run DOE and OCAP, drive continuous manufacturing improvements, mentor teams, and document best practices to improve yield and resolve recurring production issues.

Design, implement, validate, and debug system software enabling high-performance GPU-to-GPU communication and platform bring-up. Drive feature enablement, performance optimization, reliability, and cross-team collaboration for NVLink-based GPU fabrics and rack-scale AI/HPC systems.

Develop and maintain DirectX Graphics Drivers for NVIDIA GPUs, focusing on OS and chip graphics features, quality improvement, and AI-based automation solutions.

The role involves designing and maintaining AI/HPC infrastructure, focusing on large-scale networking projects, performance monitoring, troubleshooting, and customer interaction.

Design and develop safety- and security-compliant system software for the NVIDIA DRIVE automotive platform. Perform bring-up for next-gen Tegra SoC, architect scalable unified OS solutions, and debug complex interactions across the DRIVE software stack.