NVIDIA·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

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.

Questions Asked (1)

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.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

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

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AI HintsAI Generated

Suggested Approach

First, clarify the problem constraints and assumptions, such as whether node costs are positive and if the tree is static. Then, propose an efficient algorithm like depth-first search with dynamic programming to compute the minimum latency path, and discuss how to prune the tree in-place or by constructing a new pruned tree.

Pro tip: Mention that for large graphs, an iterative DFS avoids stack overflow, and consider parallelizing the search across branches if the tree is wide, leveraging NVIDIA's GPU strengths.

1. Clarify requirements and constraints

Ask about node cost properties (e.g., non-negative), tree size, memory limits, and whether pruning should modify the original tree or create a new one.

2. Define the algorithm

Use DFS with memoization to compute the minimum cost from each node to a leaf, then backtrack to identify the optimal path.

3. Implement pruning

Traverse the tree again, removing any child not on the optimal path, either by updating pointers or marking nodes for deletion.

4. Analyze complexity and trade-offs

Discuss time and space complexity (O(N) time, O(H) space for recursion), and compare iterative vs recursive approaches.

5. Consider optimizations and edge cases

Address large trees, negative costs (if allowed), and potential parallelization; handle empty tree or single-node tree.

Key Points to Mention

Dynamic programming on trees: compute min cost from each node to leaf
Depth-first search (DFS) for traversal and path reconstruction
Time complexity O(N) and space complexity O(H) for recursion stack
In-place pruning vs creating a new tree: memory and performance implications
Handling edge cases: empty tree, single node, negative costs (if applicable)
Potential parallelization using GPU or multi-threading for wide trees

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