NVIDIA·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

NVIDIA software engineer coding round, one algorithmic problem with a follow-up constraint added partway through. Pretty standard competitive-programming flavor but the follow-up tripped me up a bit.

Questions Asked (1)

A group of robots each have a score. The two highest-scoring robots are matched against each other, their scores cancel out, and only the difference (the larger minus the smaller) survives as a new robot. This repeats until one robot remains. Write a function to find the final score. A follow-up added extra constraints on top of the base problem.

Algorithms & Data Structures

Author's notes

Priority queue was the move here, pretty much immediately obvious.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem and constraints, including the follow-up. Then, propose a solution using a max-heap to efficiently extract the two largest scores, compute their difference, and reinsert it until one robot remains. Discuss time and space complexity, and consider edge cases and potential optimizations.

Pro tip: Mention that the final score is equivalent to the absolute difference between the sums of two disjoint subsets that partition the robots, which can be solved with dynamic programming for small score ranges. This shows deeper insight and prepares you for the follow-up constraints.

1. Clarify the problem and constraints

Ask questions to confirm understanding: Are scores integers? Can scores be negative? What is the size of the input? What are the follow-up constraints? This ensures you address the correct problem.

2. Propose a straightforward simulation

Describe a naive approach: repeatedly find the two largest scores, replace them with their difference, and continue until one remains. Mention that this is O(n^2) if done naively.

3. Optimize with a max-heap

Use a max-heap (priority queue) to efficiently extract the two largest scores in O(log n) time per operation, resulting in O(n log n) overall time and O(n) space.

4. Analyze complexity and edge cases

Discuss time and space complexity. Handle edge cases: empty input, single robot, all scores equal, large values, negative scores. Consider if the final score can be negative (it cannot, since we take absolute difference).

5. Address the follow-up constraints

If the follow-up involves large n or specific score ranges, propose alternative approaches like dynamic programming (subset sum) or mathematical insights. Explain trade-offs.

Key Points to Mention

Use a max-heap (priority queue) for efficient extraction of the two largest elements.
Time complexity: O(n log n) with heap, O(n^2) with naive simulation.
Space complexity: O(n) for the heap.
Edge cases: empty input, single robot, all scores equal, negative scores.
Mathematical insight: final score equals absolute difference between sums of two disjoint subsets partitioning the robots.
Dynamic programming approach for small score ranges: subset sum to find achievable differences.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.