Updated by Nvidia candidates
Senior Software Engineer, LLM Applications Interview Experience
What stood out about Nvidia was how low level they went. I got the sense they don’t want you using a lot of these high level library functions, and implementing an entire transformer from scratch was honestly what helped me most.
Interview process
I went through a pretty team-specific process for a Senior Software Engineer, LLM Applications role: recruiter screen, coding screen, short hiring manager chat, then a four-round virtual onsite. The main thing that stood out was how little they cared about generic AI talk and how much they cared about low-level details like memory, compute, parallelism, GPU utilization, and whether I actually understood things under the hood. My LLM fundamentals round felt good because I could talk through transformers, position embeddings, BERT, and even a small agent design, but the system design round was the hardest by far because they pushed into distributed training at huge scale and wanted more hands-on tool knowledge. The coding rounds were a mix of common problems and performance-minded thinking, and I got the sense they prefer people who can build from scratch rather than hide behind libraries. Overall, it felt more technical and harder to surface-prep for than most other interview loops I’ve done.
- Recruiter screen
- Technical interview
- Phone interview
- Final round
Interview tips
I’d say get hands-on and actually code, not just read or watch videos. For the LLM side, implementing a transformer from scratch helped me a lot because it forced me to understand the low-level mechanics instead of just the high-level story. For the systems side, I’d try to rent a GPU on AWS or somewhere similar and do some small-scale training so you actually run into utilization and debugging issues yourself. I’d also learn some benchmarking and debugging tools in context. Nvidia felt hard to cram for because surface-level prep just won’t carry you very far.
Company culture
It seems like Nvidia hires in a very team-by-team way. Even the recruiter sounded unusually plugged into the specific team, and the whole loop felt built around the actual work instead of a generic company interview. They seemed much more interested in depth than polish, especially around performance, distributed systems, parallelism, and whether I’d really worked close to the hardware and tooling. They also did not seem interested in buzzwordy AI answers at all. I felt like they wanted people who can actually live in the stack, not just say they work on LLMs.
Questions asked
Overview
The virtual onsite was four rounds in a row: a faster LeetCode-style coding round, a very deep distributed training system design round, an LLM fundamentals round that built from ML basics up to agents, and a behavioral round that was mostly standard except for one technical resource-allocation scenario.
Question types asked
Specific questions asked
I treated this as a straightforward implementation problem and coded the padding logic directly. This onsite coding round felt more speed-driven than the earlier screen because there were two problems and not a lot of deep follow-up discussion. It was more like solve it cleanly and move on.
Given a set of tasks with start times and a limited number of resources, how would you schedule them and reason about the shortest time to complete them?
The second coding problem was a scheduling-style question with tasks, timing constraints, and limited resources. I’d seen similar patterns before, so I approached it like a standard LeetCode scheduling problem and focused on getting to a correct implementation quickly. Compared with the first screen, this round felt more about pace because they wanted both problems done rather than a long back-and-forth on one design.
How would you handle parallelism and GPU resource management?
How would you benchmark GPU utilization?
If GPU utilization is low during training, how would you debug it?
What Nvidia tools have you used for this kind of debugging or benchmarking?
I started by laying out the training lifecycle at a high level: data, training, inference, and the need for distributed parallelism and GPU resource management at that scale. The early part felt okay, but then they went much deeper on things like benchmarking GPU utilization and debugging low utilization during training. I answered in a general way, but I got the sense they wanted hands-on familiarity with specific Nvidia tools, and that was where I felt weaker. In hindsight, this round really rewards real GPU experience, not just conceptual knowledge.
Can you explain the fundamentals behind transformers and LLMs?
What are gradient descent and backpropagation?
How do transformers work?
What are position embeddings?
What is BERT?
What are LLMs used for?
They built this round from basics upward, starting with ML fundamentals like gradient descent and backprop, then moving into transformers, position embeddings, BERT, and general LLM concepts. I felt good here because I already work with LLMs, and what helped most was that I’d actually implemented a transformer from scratch before. That made it much easier to answer beyond the buzzword level and talk about how the pieces work under the hood instead of only giving a high-level explanation.
What tools would the agent need to call?
I framed it as a simple tool-calling agent. The core idea was that the agent should call a weather tool to get the current conditions and then call a music service, basically something like Spotify, to pick music based on that weather context. It was a small application, but they were clearly checking whether I could reason about agents in a practical way instead of talking about them abstractly. This part felt pretty manageable to me.
This was the most interesting behavioral question because it was behavioral but also kind of technical. I answered it as a prioritization and resource-allocation problem, basically how I’d think about project importance, constraints, and how to allocate limited compute fairly and effectively. The rest of the behavioral round felt pretty standard, but this one stood out because it was much closer to the day-to-day tradeoffs you’d actually face in an environment with shared GPU resources.
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