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NVIDIA interview notes, page 8 of 17

FAANG / Big Tech

Most of these NVIDIA write-ups come from Software Engineers (137), then Product Managers (15) and Data Scientists (10). The most reported rounds are the technical phone screen (106), the system design round (28), the hiring manager screen (13) and the product sense round (13).

166 interview notes · updated Aug 2026

NVIDIA·Machine Learning Engineer·Technical Phone Screen

May 2026
Interviewed for an ML engineer role at Nvidia. The content was pretty thin so I can't say much about the process itself, but the core question was about how Nvidia applies machine learning internally.
  • How does Nvidia use machine learning across its products and operations?

“Broad question and I kind of rambled.”

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NVIDIA·Software Engineer·Technical Phone Screen

May 2026
Interviewed for a SWE role at Nvidia and got a pretty basic hardware fundamentals question. Short round, nothing fancy.
  • What are the key differences between RAM and ROM?

“Pretty standard stuff for a hardware-adjacent company like Nvidia I guess.”

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NVIDIA·Software Engineer·Onsite - System Design / Architecture

May 2026
NVIDIA system design round focused on the full ML compilation stack, from model definition down to GPU execution. Pretty deep technically and covered a lot of ground in one question.
  • Walk through the entire pipeline of taking a model from its high-level definition to actually running it on a GPU. Cover the frontend representation, export to an intermediate graph format, compilation to device code, and runtime execution. Then discuss common compiler optimizations like kernel fusion, quantization, and operator specialization, their trade-offs, and how your approach changes depending on whether you're targeting data-center hardware versus edge devices.

“This is a lot to cover in one question and I underestimated how much they wanted on the compiler side.”

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NVIDIA·Software Engineer·Onsite - System Design / Architecture

May 2026
Nvidia system design interview for a software engineering role. One question, big scope, and I'm still not sure I covered even half of what they were looking for.
  • How would you design a distributed training system capable of handling a trillion-parameter language model?

“I jumped straight into model parallelism and tensor sharding, which felt right, but I fumbled when they pushed on communication overhead between nodes.”

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NVIDIA·Product Manager·Onsite - Product Sense / Strategy

May 2026
Interviewed for a PM role at Nvidia and got asked about cloud gaming. Not much else to report.
  • What is cloud gaming?

“Pretty open-ended definition question.”

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NVIDIA·Product Manager·Onsite - Product Sense / Strategy

May 2026
Nvidia PM interview with a single strategy question about keeping the product lineup competitive. Pretty open-ended, which sounds like freedom until you're actually sitting there trying to structure an answer on the fly.
  • You're a PM at Nvidia. How would you keep Nvidia's product lineup competitive?

“I went straight for the GPU side and talked about staying ahead on compute density and software ecosystem lock-in, but in hindsight I think I ignored the networking and enterprise AI infrastructure angle almost completely.”

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NVIDIA·Software Engineer·Technical Phone Screen

May 2026
NVIDIA software engineer interview with a coding question that started simple and then got opened up. Pretty standard algorithmic problem but the follow-up is where things get interesting.
  • Given an integer array, return the k most frequent elements. You start with k=3, then they ask you to generalize it for any k.

“The initial version felt easy enough.”

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NVIDIA·Software Engineer·Technical Phone Screen

May 2026
Phone screen at NVIDIA for a software engineer role. The first chunk of the call was basically a forensic audit of your resume, specifically anything touching inference optimization. They want specifics, not vibes.
  • Walk me through a specific inference optimization project from your resume. What kernels did you write or tune, and what bottlenecks did you find?
  • How did you think about memory hierarchy and bandwidth when optimizing inference workloads?
  • What batching strategies did you use and what tradeoffs did you consider?
  • Describe your experience with quantization. What precision formats did you use and why?

“This is where vague answers will kill you.”

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NVIDIA·Software Engineer·Technical Phone Screen

May 2026
Interviewed for a SWE role at Nvidia and got asked about CUDA experience pretty much right out of the gate. Not a ton of content to share but it set the tone for a GPU-focused technical conversation.
  • What is your experience working with CUDA?

“Expected this at Nvidia but still fumbled the opener a bit.”

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NVIDIA·Software Engineer·Technical Phone Screen

May 2026
NVIDIA software engineer interview with a tree reconstruction problem. Nothing too wild but it's the kind of question that looks straightforward until you're actually coding it under pressure.
  • Given the inorder and postorder traversal arrays of a binary tree, reconstruct the original tree and return its root.

“The core insight is that the last element of postorder is always the root, then you find that value in inorder to split left and right subtrees.”

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