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NVIDIA interview notes, page 1 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·Software Engineer·Technical Phone Screen

Apr 2026
NVIDIA SWE coding round, one problem but it had some real depth to it. The question sounds like a basic duplicate-finding thing until you actually read the constraints.
  • Given an integer array of length n where every element is in the range [1, n-1] and at least one duplicate exists, find any duplicate number using O(1) extra space and better than O(n^2) time.

“I went straight for the cyclic sort approach since mutating the array was allowed, which worked fine.”

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NVIDIA·Software Engineer·Technical Phone Screen

Apr 2026
NVIDIA systems engineering interview that went deep on memory layout and cache behavior, basically a C++ coding question that turned into a mini lecture on hardware performance. Not what I expected from a coding round.
  • Write a C++ function to transpose a 2D matrix, and then explain the performance characteristics of your approach.

“I wrote the naive version pretty fast, swapping elements across the diagonal, felt good about it.”

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NVIDIA·Software Engineer·Technical Phone Screen

Apr 2026
NVIDIA software engineer interview that went deep on GPU architecture. One big technical question that sprawled into five or six sub-topics, felt more like a conversation than a Q&A, but I was definitely underprepared on some of the memory hierarchy specifics.
  • Compare how a CPU and GPU handle large matrix multiplication, covering core architecture, memory hierarchy, latency-hiding strategies, why matmul maps well to GPUs, and situations where a CPU might still be the better choice.

“This was basically five questions dressed up as one and I didn't realize it until I was already three minutes into talking about core counts.”

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NVIDIA·Software Engineer·Technical Phone Screen

Apr 2026
NVIDIA software engineer interview that focused on a log parsing problem with a real systems flavor to it. The question had multiple layers and they clearly wanted to see if you'd think past the naive solution.
  • Build a log parser that reads a stream or batch of log lines, counts how often each key field appears, and returns the top N most frequent items. Walk through your data structure choices, how you handle ties, and what breaks down at very large scale.

“I went straight to hash map plus a min-heap of size N and explained why you'd evict the smallest count as you go.”

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NVIDIA·Software Engineer·Technical Phone Screen

Apr 2026
Got a classic puzzle-style question during what felt like a technical screen for a software engineering role at NVIDIA. Not the kind of thing I was expecting but in hindsight it makes sense they'd want to see how you reason through optimization problems under pressure.
  • Four people need to cross a bridge at night with one flashlight. At most two can cross at a time and the flashlight must be carried across (no throwing it back). Their individual crossing times are 1, 2, 5, and 10 minutes, and two people together move at the slower person's pace. What is the minimum time to get everyone across, and what sequence achieves it? Then generalize: given n people with crossing times t1 through tn, describe a strategy for finding the minimum total time.

“I knew the specific instance from somewhere in the back of my brain, the answer is 17 minutes, but I fumbled explaining WHY for a solid two minutes.”

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NVIDIA·Software Engineer·Technical Phone Screen

Apr 2026
Interviewed for a Research Engineer role at NVIDIA and got hit with a deep ML fundamentals question about bias-variance tradeoffs. Not a casual conversation, they wanted real depth on the mechanics and the costs of each fix.
  • Walk through specific techniques to reduce bias in a model and specific techniques to reduce variance. For each one, what tradeoffs does it introduce?

“I started with the easy stuff, more expressive model reduces bias but balloons variance, more training data cuts variance but costs compute and time.”

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NVIDIA·Software Engineer·Technical Phone Screen

Apr 2026
Interviewed for a SWE role at Nvidia and got hit with a GPU architecture question pretty early on. Not the most elaborate process to describe but the technical depth required was real.
  • How does a GPU work? Walk through the architecture and what makes it different from a CPU.

“This is Nvidia so you'd think I'd have prepared harder for this.”

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NVIDIA·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
PM interview at Nvidia focused on Cosmos, specifically how you'd position and differentiate it. Pretty niche if you haven't done your homework on the product landscape going in.
  • How would you position Cosmos relative to other products within its ecosystem?
  • How would you differentiate Cosmos from what competitors are offering in the same space?

“This one tripped me up a bit because I kept conflating positioning with feature comparison.”

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NVIDIA·Product Manager·Hiring Manager Screen

Apr 2026
Interviewed for a PM role at Nvidia, just the one question about technical background. Pretty short on details but it's the kind of question that sounds easy until you're actually sitting there trying to figure out how much to lean into the technical side versus the product side.
  • What technical knowledge or background do you have that would set you up for success in this role?

“This is one of those questions where I always second-guess my calibration afterward.”

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NVIDIA·Software Engineer·Hiring Manager Screen

Apr 2026
Interviewed for a data science role at Nvidia, just one question about working with scientists. Short and a bit underwhelming as far as interview experiences go.
  • Can you describe your experience collaborating with scientists or research-oriented colleagues?

“I rambled a bit here.”

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