Principal Technical Product Manager – CUDA

Job Description

Posted on: 
July 24, 2023

As part of this role, you will develop an in-depth understanding of the overall CPU and GPU Architecture, Memory Management (HMM, UVM), programming language landscape, existing CUDA APIs, methods, and architectures for multi-node distributed computing, data locality, and migration, etc.


  • Product Definition and Architecture – Co-own NVIDIA’s strategy for positioning Grace-Hopper across Industries, and Eco-systems working very closely with other Technical Architects, Research, and Engineering Teams.
  • Strategy – Influence and participate in active discussion with Executive-Staff and Senior VPs, paving the path for the NVIDIA Grace-Hopper story.
  • Product Launches – Define the go-to-market strategy and contribute to the cross-functional implementation of the plan across Marketing, PR (Press Release), Sales, etc.
  • Asset Creation – Work with marketing to define positioning that enables the creation of technical content, including blog posts, webinars, developer tutorials, and other product value proposition tools.
  • You will need strong collaboration and communication skills along with a solid understanding of how technical decisions impact both NVIDIA and customer business.

Job Requirements

  • Master’s degree in computer science, Electrical Engineer, Applied Mathematics, or related engineering field (Ph.D. preferred) or equivalent experience
  • Experience and familiarity with scientific computing and AI (Artificial Intelligence) applications
  • 6+ years’ experience developing/architecting software, libraries, and SDKs (software development kits)
  • Additional 5+ years’ experience working as a Technical Product Manager in a Technology Company
  • World-class communication skills with a proven ability to articulate a value proposition to technical and non-technical audiences.

Ways To Stand Out from the Crowd:

  • Experience Developing/Architecting System Software
  • Experience developing/architecting parallel, heterogeneous and/or large-scale software
  • Deep understanding of system software & networking
  • Background with CUDA or GPU computing
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