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NVIDIA interview notes, page 13 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

Jun 2026
Interviewed for a software engineering role at Nvidia. Just one question about GPU programming experience, so hard to say much about the overall process.
  • Can you describe your GPU programming experience?

“Pretty open-ended.”

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NVIDIA·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Interviewed for a PM role at Nvidia. Just the one question from what I can tell, focused on where the data space is heading.
  • Where do you see the data industry heading in the next few years?

“Broader than I expected.”

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NVIDIA·Software Engineer·Recruiter / HR Screen

Jun 2026
Short Nvidia SWE screen, basically just the one motivational question from what I can tell.
  • Why do you want to work at Nvidia?

“Pretty standard opener.”

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NVIDIA·Software Engineer·Technical Phone Screen

Jun 2026
NVIDIA software engineer interview with a low-level C coding question that went deeper than I expected. They weren't just asking you to write a loop, they wanted a full library-quality design discussion on top of the implementation.
  • Implement polynomial multiplication in C. Given two polynomials as coefficient arrays with their sizes, design a library-quality function: define your own API, handle memory ownership of the result buffer, account for edge cases like zero polynomials and leading zeros, and analyze the time complexity.

“I started coding the O(n*m) nested loop pretty fast and felt good about it.”

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NVIDIA·Software Engineer·Technical Phone Screen

Jun 2026
NVIDIA software engineer interview that went pretty deep into ML compiler territory. The questions were technical and platform-specific, not the usual generic stuff.
  • What compilation optimization techniques do you apply to ML workloads, such as kernel fusion, quantization, or memory planning?
  • How does the target platform (data center GPU vs. edge device) change which optimizations you prioritize during compilation?

“This one had a lot of surface area and I kind of rambled.”

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NVIDIA·Software Engineer·Technical Phone Screen

Jun 2026
NVIDIA Linux Engineer interview, got a matrix problem that started simple enough but then they wanted me to think through the non-square case too, which is where things got more interesting.
  • Given an n x n square matrix, transpose it in place so that matrix[i][j] becomes matrix[j][i]. The solution must run in O(n^2) time and use O(1) extra space. Then explain how you'd extend this to a non-square m x n matrix and what happens to the complexity.

“The square case is pretty mechanical once you realize you only need to swap the upper triangle with the lower triangle.”

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NVIDIA·Software Engineer·Technical Phone Screen

Jun 2026
NVIDIA software engineering interview that leaned pretty heavily on Python internals and async patterns. Four distinct questions, no fluff, they clearly wanted to see if you actually understand what's happening under the hood rather than just knowing the syntax.
  • You're shown a Python helper class that repeatedly reads from an HTTP response stream and writes output. What is this class doing, and what are its responsibilities?
  • Here's a piece of async Python code that fetches multiple URLs concurrently. Find the race conditions, blocking calls inside the event loop, and any coroutines that aren't being awaited, then explain how you'd fix them.
  • Implement a function list_matching_paths(root, pattern) that returns the absolute paths of all files under a given root directory that match either a glob or regex pattern.
  • How would you safely read a large CSV file, accounting for dialect detection, encoding issues, and memory constraints, then compute basic aggregates while handling errors gracefully?

“This one felt like a warmup but I overcomplicated it.”

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NVIDIA·Software Engineer·Technical Phone Screen

Jun 2026
NVIDIA coding screen with one problem that looks manageable until you actually sit down and implement it. The encoding rules stack in ways that trip you up if you're not careful about parsing order.
  • You're given an encoded string that maps to letters a–z using three rules: a single digit 1–9 maps to the corresponding letter by position, a number followed by '#' maps to letters j–z (so '10#' is j, '26#' is z), and a parenthesized number after a token means repeat that letter that many times. Decode the full string and return a frequency array of length 26.

“I got the basic digit-to-letter mapping fine, but the '#' token tripped me up because you have to look ahead (or behind, depending on direction) to figure out if you're dealing with a one- or two-digit number.”

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NVIDIA·Machine Learning Engineer·Technical Phone Screen

Jun 2026
NVIDIA ML Engineer interview, one big technical question that basically covered three topics at once. The breadth of it was a lot to handle in one shot.
  • Walk through the architecture of a Transformer-based large language model. How does the self-attention mechanism allow the model to capture dependencies between distant tokens?
  • You have a pretrained LLM and want to fine-tune it on a domain-specific dataset. How do you approach this without causing catastrophic forgetting?
  • For an LLM deployed in production to answer user queries, how would you detect, monitor, and reduce hallucinations?

“I started with the encoder-decoder framing and then pivoted to decoder-only since that's what most LLMs actually use now.”

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NVIDIA·Data Scientist·Onsite - Multi Round

Jun 2026
Interviewed for a Data Scientist role at NVIDIA in what felt like a full-spectrum gauntlet: part behavioral, part sales strategy, part self-reflection exercise. The questions ranged from standard conflict stories to a pretty involved case about pitching GPUs to a retail CEO, which I did not see coming.
  • Why NVIDIA, and why this specific role? How does your background connect to what the team is working on?
  • Give a 60-second elevator pitch about yourself.
  • Tell me about a significant conflict within a team you were part of. What did you do, what was the result, and what would you change?
  • Describe a project that failed. What early signals did you miss, what was the root cause, and what did you change afterward?
  • Walk me through a time you handled tough objections from a customer or stakeholder and influenced a key decision. How did you map the stakeholders involved and what was the measurable impact?
  • How would you approach selling NVIDIA GPUs to the CEO of a major retailer for AI use cases? Walk through your discovery questions, value hypothesis, pilot design with success metrics, ROI model, and how you'd handle compliance risks.
  • Where do you see yourself in five years, and how does this role fit into that path?
  • Looking back at this interview process, what did you learn and how did it shift your thinking?

“I had a decent answer prepared but I leaned too hard on general GPU hype instead of tying it to anything specific on the roadmap.”

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