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NVIDIA is an American technology company founded in 1993 that invented the graphics processing unit and has become the dominant supplier of accelerated computing platforms for artificial intelligence. Its portfolio spans data centre GPUs and systems built on the Hopper and Blackwell architectures, GeForce consumer graphics, automotive and robotics platforms, high-speed networking acquired with Mellanox, and the CUDA software stack that binds the ecosystem together. Headquartered in Santa Clara, California, the company sells to cloud providers, enterprises, research institutions and gamers worldwide and is one of the most valuable listed businesses on the Nasdaq.

By submitting your resume, you’re expressing interest in one of our 2027 Deep Learning & High-Performance Computing Engineer - New College Grad roles. We’ll review resumes on an ongoing basis, and a recruiter may reach out if your experience fits one of our new college graduate opportunities. NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society - from gaming to robotics, self-driving cars to life-saving healthcare, climate change to virtual worlds where we can all connect and create.

We offer an excellent opportunity to expand your career and get hands on experience with one of our industry leading Deep Learning & High-Performance Computing teams. We’re seeking strategic, ambitious, hard-working, and creative individuals who are passionate about helping us tackle challenges no one else can solve.

Potential NCG opportunities in this field include:

  • Operator Development

  • Parallel Computing

  • AI Compiler Systems

  • Large Language Model (LLM) Inference Optimization

  • Reinforcement Learning

  • Deep Learning

  • End to End Training

  • Performance Modelling, Analysis, Projection Optimization

What we need to see:

  • Expected to graduate in 2027 with a Bachelor's, Master's, or PhD degree in Electrical Engineering, Computer Engineering, or a related field

  • Experience related to the following areas could be required:

Computer Architecture experience in one or more of these focus areas: Deep Learning, Parallel Programming, High-Performance Computing Systems, GPU Computing, GPU Architecture, CPU Architecture.

Modelling/Performance Analysis, Parallel Processing, Neural Network Architectures, GPU Acceleration, Deep Learning Neural Networks, Compiler Programming

Performance Modeling, Profiling, Optimizing, and/or Analysis

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