Jobs at NVIDIA

As a Global Trade Compliance Systems Analyst, you'll manage SAP Global Trade Services operations, maintain legal regulations, develop reports, and ensure compliance with US export regulations.

Lead a team of engineers to develop test and tools for AI-powered quality infrastructure, establish vision, and drive innovation.

Design, build, and optimize high-performance cross-platform desktop and mobile applications for GeForce NOW and NVIDIA App. Implement responsive front-end UIs, RESTful APIs, CI/CD pipelines, deployment and monitoring on AWS, and collaborate with product, design, and operations for scalable, reliable features.

Contribute to the development of simulation platforms for NVIDIA Data Center systems, enhancing simulator components and collaborating with global engineering teams.

Design, develop, optimize, debug, and maintain features for NVIDIA GPUs. Collaborate with teams, resolve customer issues, and improve documentation.

The role involves developing infrastructure for AI applications, managing deployment processes, analyzing data, and collaborating with developers to optimize automation systems in the Windows ecosystem.

Design, implement, operate and support large-scale GPU cloud PaaS and backend microservices. Drive architecture, test automation, CI/CD, DevSecOps, observability, and collaborate with frontend, UX and stakeholders to deliver highly available, performant distributed systems.

Design and evaluate hardware architectures for parallel deep learning performance. Benchmark and analyze AI workloads (single- and multi-node). Build profiling, analysis, and debugging tools in Python/C++. Collaborate with architecture teams and product management and track emerging deep learning research and trends.

The role involves developing compilers for NVIDIA GPUs, working on optimization problems, collaborating with a distributed team, and influencing new GPU architectures.

Lead research and development of state-of-the-art generative audio and multi-modal deep learning models; define technical roadmap; scale and optimize large foundation models on GPU clusters; develop audio-speech-visual fusion algorithms; productize and deploy optimized inference on NVIDIA GPUs/edge; collaborate across research, hardware, and product teams; mentor engineers and uphold code and design quality.

Provide Tier 3 network support for data center/AI/storage/HPC environments: troubleshoot routing and switching issues, manage complex installations, respond to customer inquiries, perform site visits, and improve internal support processes while collaborating across teams to ensure customer satisfaction.

Support and improve NVIDIA AI Enterprise customer deployments. Develop tools and documentation, troubleshoot issues, and collaborate with engineering for resolutions.

Design and develop algorithms for map-based driving products, architecture design for map integration, and implement evaluations of large scale models for autonomous vehicles.

The role involves managing the SWQA team, developing automation testing strategies, and ensuring high-quality product releases for NVIDIA's GPU products. Responsibilities include defining test engineering strategies, building automated tests, and leading cross-functional collaboration, while driving improvements in QA methodologies and workflows.

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.

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

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

Join NVIDIA's Software Infrastructure team as a Senior DevOps Engineer, driving automation, managing CI/CD systems, and enhancing cloud infrastructure for GPU and Automotive divisions.

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.