Nvidia Interview Help

I’m interviewing next week for the Senior Deep Learning Algorithms Engineer role. Brief background: 5 years in DL; Target (real-time inference with TensorRT & Triton, vLLM), previously Amazon Sear

I’m interviewing next week for the Senior Deep Learning Algorithms Engineer role. Brief background: 5 years in DL; Target (real-time inference with TensorRT & Triton, vLLM), previously Amazon Search relevance (S-BERT/LLMs). I’m strengthening GPU architecture (modal glossary), CUDA (from my git repo have some basic CUDA concepts and kernels), and TensorRT-LLM (going through examples from github) prep. If you have a moment, could you share: 1. How the rounds are usually structured (coding, CUDA/perf tuning, system design)? 2. Topics that get the most depth (e.g., memory hierarchy, occupancy, kernel optimization, Tensor Cores)? 3. Any do’s/don’ts you wish candidates knew? 4. What topics to revise quickly in DSA?