NVIDIA Software Engineer Interview Questions

Situation: We had a high-priority release where one component was suddenly using far more CPU than expected. Task: I needed to find the root cause quickly without destabilizing the release. Action: I narrowed the issue down with profiling, compared the hot path before and after the change, and found that a repeated allocation pattern had been introduced in a critical loop. I worked with the team to replace it with a reused buffer and added a benchmark so we could catch regressions earlier. Result: CPU usage dropped significantly, the release stayed on schedule, and the team adopted the benchmark as part of the normal review process.

Situation: On one project, the team wanted to ship quickly with a design that I believed would create serious scaling problems later. Task: My responsibility was to raise the concern without blocking the team or making the discussion personal. Action: I prepared a short comparison showing the likely failure mode, the expected cost of fixing it later, and a lower-risk alternative that only required a small amount of extra work. I stayed focused on the product impact and the operational risk rather than on proving I was right. Result: The team chose the safer design, and we avoided a future rewrite that would have been much more expensive.

I would first think about checking every subarray, but that would be too slow. To optimize it, I would use prefix sums and a hash map that stores how many times each prefix sum has appeared. The key idea is that if two prefix sums differ by k, then the subarray between them sums to k. The reason I choose this approach is that it gives O(n) time and handles negative numbers correctly. I would also mention edge cases like k equal to zero and arrays with repeated values.

Before designing it, I would clarify how much log volume the system must handle, whether logs must be searchable in real time, and how much data loss is acceptable. At a high level, I would keep logging asynchronous so the application thread does not block, buffer logs locally, and ship them to a collector in batches. The reason I choose batching is that it reduces overhead and fits high-throughput environments better than sending every log line immediately. I would also think about log sampling, compression, and what happens when the network is unavailable. The trade-off is between visibility and runtime overhead, so I would keep the critical path lightweight.

I would compare the start of one interval with the end of the other. If the maximum of the starts is less than or equal to the minimum of the ends, then they overlap; otherwise, they do not. The reason I choose this simple check is that it is easy to reason about and O(1) time. I would also mention whether touching endpoints count as overlap, because that depends on the problem statement. Edge cases include zero-length intervals and reversed inputs if the data is not normalized.