Jobs at NVIDIA

Design, implement, and maintain infrastructure and CI/CD pipelines to deploy and automate local AI workloads across inference backends. Build model download and repository sync systems, implement data processing and visualizations, measure accuracy and performance, debug existing frameworks, and collaborate with developers to streamline debugging and deployment of AI applications and models.

Lead and grow a team building an on-device AI inference platform for RTX/ DGX GPUs. Drive cross-functional alignment, architect inference runtimes, optimize models and pipelines for low-latency, memory-efficient local deployment, and establish processes for system-level debugging, performance/accuracy trade-offs, and production readiness. Mentor engineers and deliver roadmap-aligned, high-quality releases.

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.

Design and optimize on-device AI inference stacks for RTX and DGX systems. Build inference runtimes, optimize models and pipelines (quantization, pruning), perform system-level debugging and performance-accuracy tradeoffs, and collaborate across software, research, architecture, and product teams to ensure production readiness.

Develop and optimize on-device AI inference stacks and runtimes for RTX and DGX systems. Collaborate with cross-functional teams to design high-performance, memory-efficient inference pipelines, apply model optimizations (quantization, pruning, distillation), perform system-level debugging and performance tuning, and build infrastructure for performance/accuracy evaluation and production readiness.

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

Design and develop low-latency, GPU-accelerated video streaming features; optimize encoder and GPU/CPU pipeline performance; build video quality measurement and analysis tools; integrate AI models for real-time video processing and adaptive streaming; and improve reliability, telemetry, and debugging for cloud-based streaming systems.

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.

Architect, develop, and debug system software for NVIDIA Halos across QNX, Linux, and hypervisor environments. Build BSP, boot, drivers, device management, I/O virtualization, and diagnostics for NVIDIA SoCs. Ensure safety and security compliance (ISO 26262/21434), apply formal methods, optimize platform performance, use AI-assisted tools, design tracing/logging solutions, and mentor junior engineers.

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.

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.

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

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.

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 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.