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Tier-1 brand and metro location but niche PhD wireless ML specialization reduces applicant density.
Requires PhD-level wireless PHY/MAC and ML specialization, so candidate backgrounds are highly domain-specific.
PhD enrollment, wireless L1/L2 and ML expertise, programming and CUDA requirements make shortlisting highly strict.
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Develop and optimize AI/ML modules for wireless Layer1/Layer2 signal processing functional blocks in NVIDIA Aerial RAN software.
Analyze and select appropriate ML architectures for RAN functions, iteratively train and modify models to improve Over-The-Air (OTA) performance and computational efficiency.
Collaborate with multi-functional teams including DevTech to benchmark OTA performance and optimize compute needs across NVIDIA computing platforms.
Currently a full-time PhD student specializing in AI and Wireless domains.
Ability to intern for at least 6 months starting last week of January 2026.
Strong understanding of wireless Layer1/Layer2 functions and algorithms.
Proficiency in AI/ML techniques including Transformers and CNNs, programming skills in C/C++, and experience simulating signal processing algorithms in Matlab and Python.
Experience analyzing and selecting ML model architectures specifically for signal processing domains.
Exposure to real-time, latency-sensitive programming on CPU, GPU, DSP, or vector processors is advantageous though not mandatory.
Familiarity with CUDA programming and NVIDIA GPU architectures is a strong plus.