CUDA & GPU Programming is writing low-level parallel code to run computation directly on GPU hardware, the core skill an ML Infrastructure Engineer needs to squeeze throughput out of training and inference clusters. Indian teams at companies scaling large model workloads pay a premium for engineers who can optimize kernels rather than just call PyTorch APIs, since GPU hours are the biggest line item in ML infra budgets. It's usually learned through parallel-computing coursework or NVIDIA's own CUDA training after a strong C/C++ foundation.
The skills most often needed alongside CUDA & GPU Programming in the same roles — build these together to widen your options.
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