NVIDIA's hiring volume has surged with the AI buildout, but the interview bar hasn't softened. Most roles go through a recruiter screen, then a technical phone round, then an onsite (or virtual onsite) of 4-6 interviews covering coding, system design, domain depth, and behavioral. For SWE and infrastructure roles, expect LeetCode-style problems weighted toward medium/hard, with real emphasis on complexity analysis. System design rounds often have a GPU or distributed-compute angle: questions about memory bandwidth, parallel workloads, or scheduling at scale aren't unusual even for non-GPU roles. For ML and AI research roles, domain knowledge matters a lot. They'll probe your understanding of transformer architectures, CUDA fundamentals, or training infrastructure depending on the team. Behavioral rounds tend to focus on scope, influence, and how you've navigated ambiguity or conflict. NVIDIA values deep ownership. Across functions, the culture rewards people who go deep, own outcomes end to end, and push back when they have data. The company has grown fast and the experience varies a lot by team and hiring manager. Timelines can run 4-8 weeks. Read the full Primly report at /community/behavioral-interview-questions/nvidia. (Posted by Primly Team, based on aggregated community reports.)