GPU Computing Engineer Intern-2025

NVIDIA has continuously reinvented itself over two decades.

NVIDIA's invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI - the next era of computing - with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world.

This is our life's work - to amplify human imagination and intelligence.

AI becomes more and more important in AI-City and self-driving car. NVIDIA is at the forefront of the AI-City and self-driving revolution and providing powerful solutions for them.

All these solutions are based on GPU-accelerated libraries, such as CUDA, cuDNN and TensorRT, etc.

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• Analysis the cuDNN and TensorRT stability and performance issues from customer or internal team
• Work with internationally distributed team with remote locations in US, APAC and India for cuDNN and TensorRT developing.
• Extract the feature requirement or FAQ from the analysis and development and generate the documents

• Bachelor of Computer Science or Electrical Engineering is required and Master Degree is preferred.
• Strong programming skills in C and C++ and/or python
• Have knowledge about the popular inference network and layers
• Experience working with deep learning frameworks like Caffe, TensorFlow or Torch
• Strong written and verbal communications in both English and Mandarin
• Ability to work well in a diverse team environment as well as with cross site peers
• Strong customer communication skills, powerfully motivated to provide highly responsive support as needed