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NVIDIA interview notes, page 14 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

Jun 2026
System design round at NVIDIA for a software engineer role. The whole thing basically came down to one deep question about Kubernetes internals and I was not as prepared as I thought I was.
  • Walk me through what Kubernetes Operators and Custom Resource Definitions are, how they work together, and what separates a well-built Operator from a mediocre one.

“This question is way bigger than it sounds.”

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NVIDIA·Software Engineer·Technical Phone Screen

Jun 2026
NVIDIA SWE interview that went deep into ML framework internals and GPU compilation pipelines. Not your typical coding round, this was more of a technical discussion about how the stack actually works under the hood. Felt underprepared on a few specifics.
  • What are the high-level trends in ML frameworks right now, and how has the ecosystem evolved from NumPy through PyTorch to JAX? What are three concrete differences between PyTorch and JAX?
  • Walk through the lifecycle of a model from definition to execution on a GPU, covering the frontend, intermediate representation like an ONNX computation graph, and the compilation steps involved.
  • What optimization techniques are applied when compiling a model for GPU execution? Specifically discuss kernel fusion, quantization, and any other relevant methods.
  • Are you familiar with the differences between data-center hardware and edge hardware, and how do those constraints shape compilation and deployment decisions?

“I had opinions on this but fumbled the three-differences part.”

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NVIDIA·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed for a SWE role at Nvidia and got asked about AR/VR experience. Pretty niche focus for a general SWE screen, wasn't totally sure what they were looking for.
  • What experience do you have working with AR or VR technology?
  • Have you shipped or contributed to any AR or VR projects?

“Wasn't expecting this to come up so directly.”

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NVIDIA·Software Engineer·Technical Phone Screen

Jun 2026
NVIDIA systems interview, came down to a pretty gnarly question about memory layout and union tricks for a custom string class. Not a lot of fluff, just dove straight into the technical stuff.
  • You have a small-string-optimized string class where the inline buffer and a heap pointer coexist as separate fields. If strings frequently exceed the inline buffer size, the pointer field is always in use and the buffer bytes are wasted. How would you reduce the overall struct size?

“The answer they were looking for is collapsing the inline buffer and the pointer into a union so they share the same memory, then using a flag to track which one is active.”

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NVIDIA·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed for a Software Engineer role at NVIDIA and got hit with the classic 3Sum problem. Nothing too exotic but the deduplication part is where I fumbled a bit under pressure.
  • Given an integer array, find all unique triplets that sum to zero.

“I knew the two-pointer approach going in but spent way too long second-guessing the dedup logic.”

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NVIDIA·Software Engineer·Technical Phone Screen

Jun 2026
NVIDIA SWE interview with a coding problem that looks like a cache question but has a storage-size twist. The core challenge was building an in-memory disk space manager from scratch, which ended up being more nuanced than I expected once eviction logic came into the picture.
  • Design and implement an in-memory disk space manager that stores datasets by ID with a given size. It has a fixed total capacity, supports put (with eviction if needed) and get operations, and must handle updates to existing datasets.

“My first instinct was LRU cache and I basically started coding before fully thinking through the size-based eviction.”

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NVIDIA·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at NVIDIA for a software engineering role. The whole thing was one long distributed systems question that kept branching into follow-ups. Left feeling like I'd covered maybe 60% of what they wanted.
  • Design a distributed counter service that supports atomic increment/decrement and read-after-write consistency, scales horizontally, and stays correct under retries and node failures. Walk through the data model, APIs, and concurrency control approach.
  • How would you handle idempotency and exactly-once vs at-least-once semantics in this counter service, especially under retries?
  • Describe your approach to leader election and partition tolerance for this service.
  • How do you deal with clock skew in a distributed counter system?
  • What monitoring and rollback strategies would you use if a counter shard becomes a contention hotspot?

“Started with a simple key-value model and CAS operations, which felt right, but then they pushed on horizontal scaling and I fumbled a bit trying to explain per-key sharding with a single writer per shard.”

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NVIDIA·Product Manager·Hiring Manager Screen

Jun 2026
Interviewed for a PM role at Nvidia and got a question early on about 3D or foundational physics background, which felt more like a filter than a real conversation.
  • What experience do you have with 3D concepts or foundational physics from your previous roles?

“Wasn't expecting this to come up so directly.”

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NVIDIA·AI Engineer·Onsite - System Design / Architecture

Jun 2026
NVIDIA AI Engineer interview that went deep into virtualization internals. Not what I expected for an AI role but apparently they care a lot about systems fundamentals. Left feeling like I'd studied the wrong half of my notes.
  • Walk me through how virtual machines work at a systems level, covering hypervisor types, CPU and memory virtualization, I/O handling, and how VMs compare to containers.

“This was basically a 'show me everything you know' question and I fumbled the sequencing badly.”

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NVIDIA·Software Engineer·Technical Phone Screen

Jun 2026
NVIDIA interview for a software engineer role that leaned more product/technical writing than I expected. The question was about launching an ML technique and how you'd approach both discovery and developer adoption. Felt like a cross between a PM case and a technical strategy question.
  • You have a new ML technique to bring to market. Before writing any technical documentation or blog content, what discovery questions would you ask the product and engineering team? And how would you build a case for developer adoption, covering things like latency/accuracy benchmarks, demos, migration paths, and ROI?

“Two questions stapled together, which threw me off a bit.”

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