Manager, CUDA Driver

We are looking for a software engineering manager with strong leadership and mentoring skills to join the CUDA Driver team! We design the CUDA programming model, expose that programming model through the CUDA Driver & CUDA Runtime APIs, and implement the associated functionality in the CUDA Driver & CUDA Runtime. The CUDA Driver and its programming model serve as the foundation for NVIDIA’s CUDA-X software stack and compute platform. The team primarily works on aspects of the CUDA Driver related to its memory model – how to allocate, move, and access memory in a performant and efficient manner across NVIDIA’s range of hardware offerings, including both single-node and multi-node environments. 

What you will be doing: 

• Lead a team of system software engineers to design and build CUDA for current and future hardware architectures. 

• Engage multi-functionally with teams across NVIDIA to define and drive CUDA’s roadmap. 

• Contribute to CUDA feature design and drive adoption of new CUDA functionality. 

• Establish team objectives, prioritize incoming work, mentor team members, and manage team performance. 

• Monitor team execution, identify opportunities for process improvement, and take the lead on implementing any identified improvements. 

What we need to see: 

• 8+ overall years of experience in the software industry with 3+ years of management experience  

• Bachelors, Masters, or Ph.D. or equivalent experience in Computer Science, Computer Engineering, or a related field. 

• Strong system software fundamentals, hands-on C programming and debugging skills. 

• Ability to manage a team’s execution towards project deliverables, particularly in environments with competing priorities. 

Ways to stand out from the crowd: 

• In depth understanding of computer architecture and memory subsystems. 

• Experience implementing or directly using GPU programming APIs, such as CUDA, OpenCL, OpenGL, or Vulkan. 

• Background with distributed systems or high-performance clusters. 

• Experience with Deep Learning frameworks such as PyTorch, TensorFlow, or vLLM.