PhD Intern, AI ML in Wireless L1/L2 - Fall 2026 at Nvidia

Our work at NVIDIA is dedicated towards a computing model focused on visual and AI computing. For two decades, NVIDIA has pioneered visual computing, the art and science of computer graphics, with our invention of the GPU. The GPU has also shown to be spectacularly effective at solving some of the most complex problems in computer science. Today, NVIDIA’s GPU simulates human intelligence, running deep learning algorithms and acting as the brain of computers, robots and self-driving cars that can perceive and understand the world. We are looking to grow our company and teams with the smartest people in the world and there has never been a more exciting time to join NVIDIA. NVIDIA Aerial CUDA Accelerated RAN (ACAR) is framework for building high-performance, software-defined, cloud-native Radio Access Network functions over NVIDIA CPU/GPU/DPU based systems, which will drive our AI native 6G solutions. We are seeking a self-motivated Intern to drive the adoption of AI/ML functions in the Phy and Mac layers of our Aerial SW. This position offers the opportunity to work on cutting-edge technology, using NVIDIA's world-class compute platforms to advance the field of AI native wireless stack to achieve the Spectral and Energy efficiency goals of 6G! What you'll be doing: As a member of Aerial RAN team working on AI Native stacks, you will be contributing to Develop and Optimize AI / ML modules for functional blocks specifically in wireless signal processing Perform literature survey to understand the prior art on AI/ML for RAN Analyze and identify the suitable ML architecture for the RAN functions of interest. Identify the right ML Architecture, complexity for each of the functional blocks Collaborate with multi-functional teams to optimize the OTA performance and compute complexity with DevTech and other business units within NVIDIA Benchmarking of OTA performance improvements with AI models and compute needs on different platforms Iteratively train, test & modify Model A