Nvidia PhD Intern AI ML Wireless L1/L2 - Spring 

India, Karnataka, Bengaluru 

181028197

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 Arch for performance improvements

What we need to see:

• Full time PhD student doing research in the fields of AI and Wireless domains, and able to work as an Intern for at least 6 months or more starting from last week of January 2026

• Thorough understanding of the wireless Layer1/Layer2 functions and algorithm aspects

• Excellent grip on AI and ML concepts, techniques and abreast of latest developments in this field

• Deep understanding of Transformers, CNNs and other ML Architectures and their use cases

• Hands on experience in simulating signal processing algorithms in Matlab and Python.

• Programming skills in C/C++

• Experience in analyzing the problem, identifying the right model architectures. developing Models, Training and Optimization, preferably on signal processing domains

from the crowd:

• Knowledge of CPU, DSP or GPU architecture, as well as memory, I/O and networking interfaces.

• Experience with programming latency sensitive, real-time, multi-threaded applications on CPUs and one or more of GPUs or DSPs or Vector processors.

• Appetite to learn the details of how next generations of GPU will operate and build an outstanding Software-Radio 5G/6G stack that can fully demonstrate their power.

• Familiarity with CUDA programming and NVIDIA GPU Architectures