Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing, an era in which our GPUs act as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent. As an NVIDIAN, you'll be immersed in a diverse, encouraging environment where everyone is inspired to do their best work.  

Our GPU Architecture team sits at the heart of this innovation, building and validating the simulators, models, and testing infrastructure that shape how future GPUs think, compute, and perform. In this position, you'll be responsible for developing and enhancing features in the GPU architecture. This role is on the GPU Architecture team, contributing to SM (Streaming Multiprocessor), Tensor Core, Work Distribution, and Ray Tracing architecture development. 

What you'll be doing 

• Advancing GPU architecture simulators, testing infrastructure, and performance metrics 

• ​Modeling graphics and parallel processing features for future GPU architectures in C++ and Python 

• Developing test plans and testing infrastructure to validate and verify functional models 

• Debugging tests on architecture simulators and building tools to ensure model accuracy 

What we need to see 

• Currently pursuing or recently completed an MS or PhD in Computer Science, Electrical Engineering, Mathematics, or a related field (or equivalent experience)

• Strong programming ability in C++ and Python 

• Deep knowledge of computer architecture and parallel programming 

• Strong fundamentals in data structure and algorithms  

Ways to stand out from the crowd 

• Background in machine learning, deep learning and graphics algorithms 

• Prior research or internship experience in GPU or computer architecture 

• Programming experience with CUDA DirectX, and/or Vulkan 

• Experience using AI-assisted development tools to improve engineering productivity while maintaining strong technical judgement, validation, and code quality