NVIDIA has continuously reinvented itself for over two decades. The invention of the GPU in 1999 propelled the growth of PC gaming, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning has ignited modern AI, positioning NVIDIA as a leading AI computing company. There is a growing focus on delivering AI models locally, closer to the source of data. This reduces latency, improves real-time processing, and addresses privacy concerns by minimizing data transfer to centralized servers. As technology advances, client-side AI (local execution) will play a key role in crafting digital experiences.  

The Local AI team is seeking a System Software Manager to lead development of an efficient on-device AI software stack. The software stack will support RTX, RTX Pro, and DGX-class systems. This role focuses on high-performance local inference, agentic workloads, low latency, efficient memory use, scalable infrastructure, practical deployment on resource-constrained platforms, and delivering a streamlined out-of-box experience for developers and end users.  

What you'll be doing:

• Lead and grow a team building the on-device AI inference platform for RTX, RTX Pro, and DGX GPUs, with accountability for execution, technical direction, delivery quality, and roadmap alignment.  

• Drive cross-functional alignment with NVIDIA’s software, research, architecture, and product teams, along with industry partners and open-source communities, to build strategy and strengthen the AI ecosystem across RTX and DGX platforms. 

• Provide technical leadership for the architecture and evolution of modern inference runtimes and execution stacks across frameworks such as Llama.cpp, vLLM, PyTorch, WinML, DXCGC, and TensorRT-RTX, spanning workloads including LLMs, vision-language models, TTS, ASR, and diffusion models.  

• Mentor engineers, develop technical leaders, and foster a high-performance team culture centred on innovation, collaboration, and operational excellence. 

• Coordinate end-to-end optimization of AI models, data pipelines, and inference runtimes to improve performance across current and next-generation GPU architectures. 

• Drive adoption of model optimization techniques such as quantization, pruning, sparsity, and distillation to enable efficient deployment of large models on local and edge devices. 

• Establish team processes for system-level debugging, performance optimization, and performance-accuracy trade-off analysis, including infrastructure for performance and accuracy sweeps, gap analysis, and production-readiness improvements. 

Ways to stand out from the crowd :

• Strong understanding of modern machine learning, deep neural networks, and generative AI, along with contributions to notable open-source projects. 

• Demonstrated success building teams, setting technical vision, and scaling execution through periods of rapid growth. 

• Track record of delivering end-to-end products with geographically distributed teams in multinational product organizations. 

• Experience in lower-level systems or GPU programming, including CUDA and high-performance systems development. 

• Contributions to open-source inference runtimes, model tooling, or performance infrastructure as well as practical experience working with frameworks and APIs including Llama.cpp, PyTorch, TensorRT, Vulkan, DirectX, and vLLM. 

We're a top employer known for innovation and growth. We are an equal-opportunity employer and value diversity at our company. With competitive salaries and a generous benefits package, we are widely considered to be one of the world’s most desirable employers of technology. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our best-in-class engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we would like to hear from you.