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NVIDIA·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Phone screen for a software engineering role at NVIDIA, Linux-oriented. Two coding problems and apparently a C code review, though the C snippet itself wasn't shared so I can't say much about that part. Pretty standard technical screen but the key-value store design had some nuance worth thinking through.

Questions Asked (2)

Q1

Given an n x n matrix, write a function to return its transpose (swap rows and columns so that result[i][j] equals the original matrix[j][i]).

Algorithms & Data Structures
Author's notes

Fairly mechanical once you see it.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints (e.g., in-place vs. new matrix, square matrix) and then present a clean solution that iterates over the upper triangle and swaps elements. Discuss time and space complexity, and mention potential optimizations for cache efficiency.

Pro tip: For in-place transpose, iterate only over the upper triangle (j > i) to avoid redundant swaps. Also, consider cache performance: accessing columns in a row-major language like C/C++ can be slow, so blocking or tiling may be beneficial for large matrices.

1. Clarify requirements

Ask whether the matrix is square, whether it should be transposed in-place or a new matrix returned, and if there are any constraints on time/space.

2. Choose approach

Decide between in-place (swap elements) and out-of-place (create new matrix). For in-place, iterate over the upper triangle and swap with the lower triangle.

3. Implement solution

Write code that correctly swaps elements. For in-place: for i from 0 to n-1, for j from i+1 to n-1, swap matrix[i][j] and matrix[j][i].

4. Analyze complexity

State that time complexity is O(n^2) and space complexity is O(1) for in-place, O(n^2) for out-of-place.

5. Discuss optimizations

Mention cache-friendly techniques like blocking/tiling for large matrices, and note that the problem is memory-bound.

Key Points to Mention

  • In-place vs. out-of-place trade-offs
  • Time complexity O(n^2) and space complexity O(1) for in-place
  • Iterating only over the upper triangle to avoid redundant swaps
  • Cache performance and memory access patterns
  • Edge cases: 1x1 matrix, empty matrix, non-square matrices (if allowed)
  • Potential use of blocking/tiling for large matrices

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

Q2

Design an object-oriented key-value store that supports set(key, value), get(key), and a setAll(value) operation that updates every existing key at once. Walk through the time complexity of each operation.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

This is where it gets interesting.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a class with a versioned timestamp approach to achieve O(1) for all operations. Explain the trade-offs between different designs, such as naive iteration versus versioning, and analyze time complexity for each operation.

Pro tip: Mention that the versioning approach is used in real systems like Redis for efficient bulk updates, and discuss how it handles edge cases like setAll before any set.

1. Clarify Requirements

Ask about expected data size, concurrency needs, and whether setAll should affect future keys. Confirm that setAll updates all existing keys, not future ones.

2. Propose Naive Solution

Describe a simple hash map where setAll iterates over all keys, resulting in O(n) time for setAll. Discuss its limitations.

3. Design Optimized Solution

Introduce a versioned timestamp approach: maintain a global version and lastSetAllVersion, and store each key's value with the version at which it was set. For get, compare versions to decide whether to return the stored value or the global setAll value.

4. Analyze Time Complexity

Explain that set, get, and setAll are all O(1) time on average, with O(1) space per key. Contrast with the naive O(n) setAll.

5. Discuss Trade-offs and Extensions

Mention memory overhead of storing versions, handling of deletions, concurrency considerations, and possible variations like lazy propagation.

Key Points to Mention

  • Use of a global version counter and per-key version to avoid iterating over all keys
  • O(1) time complexity for set, get, and setAll in the optimized design
  • Handling of edge cases: setAll before any set, set after setAll, and get for non-existent keys
  • Space complexity: O(n) for n keys, with additional constant space for version tracking
  • Comparison with naive approach and why it's inefficient for large datasets
  • Potential concurrency issues and how to address them (e.g., locks or atomic operations)

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