NVIDIA interview notes, page 2 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·Onsite - System Design / Architecture

Apr 2026

System design round at NVIDIA for a software engineer role. The question was essentially one big container/CI-CD deep dive that kept branching into sub-topics I wasn't fully prepped for.

Walk through the full lifecycle of a container image in a CI/CD pipeline, from writing a Dockerfile and building the image, through pushing it to a registry, all the way to how a node actually pulls and runs it at deploy time.

“This felt manageable at first and then just kept expanding.”

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NVIDIA·Software Engineer·Technical Phone Screen

Apr 2026

NVIDIA SWE interview with a tree-based DP problem involving neural network computation graphs. Pretty niche stuff, felt more like a systems/ML infra question than a typical coding round.

You're given a tree-shaped computation graph where each node is a neural network operation with a latency cost. Find the path from root to leaf that minimizes total latency, then prune all branches not on that path.

“The tree DP clicked pretty fast once I stopped thinking about it as a graph problem.”

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NVIDIA·Software Engineer·Hiring Manager Screen

Apr 2026

Thirty-minute screen with the hiring manager at NVIDIA for a Design Engineer role. Pretty standard background conversation but they really wanted you to connect your past work to actual impact, not just describe what you did.

Walk me through your background and how your career has progressed to this point.
Tell me about one or two projects you're most proud of. What was the problem, what did you do specifically, and what were the outcomes?
What questions do you have for me about the team, what they're working on, and what they need most from this hire?

“I rambled a bit here.”

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NVIDIA·Software Engineer·Technical Phone Screen

Apr 2026

NVIDIA systems interview, one deep question about string comparison performance that turned into a whole conversation about CPU cache behavior. More low-level than I expected going in.

For a string comparison function, why is comparing two short strings (under 256 bytes) meaningfully faster than comparing two long strings, even if you control for the difference in length? Walk through what's happening at the hardware level.

“This one went deeper than I initially gave it credit for.”

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NVIDIA·Software Engineer·Technical Phone Screen

Apr 2026

NVIDIA software engineer interview that was basically one long graph design problem. They wanted a full mini API built out and then kept pushing on efficiency and edge cases until I ran out of things to say.

Design and implement a small graph API with three functions: one to insert nodes and edges, one to apply configuration to the graph (like setting attributes or marking dependencies), and one to validate the graph against a set of structural requirements.
What is the time and space complexity of your validation logic, and how would you make repeated validations more efficient if the graph is being updated frequently?

“This felt manageable at first but the scope kept expanding.”

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NVIDIA·Software Engineer·Technical Phone Screen

Apr 2026

Interviewed for a SWE role at Nvidia and got asked about ray tracing experience. Pretty niche question that felt more like a conversation starter than a deep technical dive.

What experience do you have with ray tracing in video games?

“Made sense given it's Nvidia, but I still fumbled a bit trying to connect my background to something this specific.”

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NVIDIA·Software Engineer·Technical Phone Screen

Apr 2026

Technical screen for a Research Engineer role at NVIDIA, focused almost entirely on ML monitoring and production model health. One meaty question that branched into a bunch of sub-topics.

What is model drift, and how do you differentiate between covariate drift, label drift, and concept drift? How would you detect each type in a production system, and what would trigger a retraining decision?

“This started as a definitions question and turned into a full system design conversation before I realized it.”

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NVIDIA·Product Manager·Technical Phone Screen

Apr 2026

Interviewed for a PM role at Nvidia, two questions focused on how you think about measuring success for LLM and RAG projects. Pretty technical for a PM screen but not surprising given the company.

How do you measure whether an LLM or RAG project you worked on is efficient?
How do you measure model quality for an LLM-based product?

“I went straight to latency and cost per query, which felt right, but then stumbled when they pushed on what 'efficient' actually means in context.”

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NVIDIA·Software Engineer·Technical Phone Screen

Apr 2026

Deep technical interview at NVIDIA for a software engineering role, basically one long open-ended question about ML infrastructure from training to inference. No coding, just a sprawling conversation where they wanted to see how you think about trade-offs across the whole stack.

Walk through how you would optimize a large-scale model training and inference pipeline end to end, covering distributed training strategies, memory management, precision formats, custom kernels, communication primitives, inference serving techniques, and how you'd profile and prioritize improvements.

“This is a monster of a question and I did not pace myself well.”

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NVIDIA·Software Engineer·Technical Phone Screen

Apr 2026

Technical phone screen at NVIDIA for a software engineering role, pretty deep into ML fundamentals and GPU compute. The question was one long multi-part thing about MLP forward passes and PyTorch internals, which felt more like a grad school exam than a typical SWE interview.

Walk through a full MLP forward pass: write out the linear transform Y = XW + b with explicit shapes for W and b, explain how PyTorch broadcasting handles a bias vector of shape (d_h,) across a batch, chain through multiple layers with a nonlinear activation like ReLU while tracking shapes at each step, and explain how these operations vectorize on GPU. Also discuss the differences between torch.matmul, the @ operator, and nn.Linear, and common pitfalls with shapes like (B, 1) vs (B,).

“This was a single question but it just kept going.”

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