Scale AI·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Scale AI SWE coding round, one problem the whole session. Matrix simulation with a follow-up asking you to redo it in O(1) space. The problem itself was fine but the space optimization part is where things got interesting.

Questions Asked (1)

Given an m x n integer matrix representing neurons (0 = firing, >0 = non-firing), update each cell simultaneously based on how many of its 8 neighbors are firing. Rules: a firing cell with exactly 3 firing neighbors becomes 6; a non-firing cell with 0 or 1 firing neighbors loses 2; a non-firing cell with more than 3 firing neighbors loses 1; values can't go below 0; otherwise no change. First implement with a copied matrix, then optimize to O(1) auxiliary space.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The copy-based version came together pretty quickly.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the rules and edge cases, then present the straightforward solution using a copied matrix to ensure simultaneous updates. For the O(1) space optimization, encode both the original and new states in each cell using additional bits (e.g., bitwise operations) to avoid extra space, and explain the encoding/decoding process clearly.

Pro tip: Demonstrate awareness of the trade-off between code clarity and space optimization: mention that the copied matrix approach is simpler and less error-prone, while the O(1) method is more complex but necessary for memory-constrained environments. Also, test with edge cases like 1x1 matrix and all firing/non-firing cells.

1. Clarify rules and edge cases

Confirm the neighbor definition (8-directional), simultaneous update requirement, and value constraints (non-negative). Ask about matrix size limits and whether in-place modification is allowed.

2. Design copied matrix solution

Create a copy of the input matrix to read original states from, then update the original matrix based on neighbor counts from the copy. Explain time complexity O(m*n) and space O(m*n).

3. Devise O(1) space encoding

Use bit manipulation to store both original and new states in each cell. For example, use the lowest bit for original firing state (0 or 1) and higher bits for new value, or use two bits per cell if values are small.

4. Implement and verify encoding/decoding

Write code that first encodes original state, then computes neighbor counts using encoded values, updates cells with new state, and finally decodes to final values. Walk through a small example to verify correctness.

5. Analyze trade-offs and test

Compare the two approaches in terms of time, space, and code complexity. Discuss potential pitfalls like integer overflow and ensure simultaneous update is maintained. Test with edge cases.

Key Points to Mention

Simultaneous update requirement and how copying or encoding ensures it
Time and space complexity analysis for both approaches
Bitwise encoding technique for O(1) space (e.g., using bits to store original and new states)
Handling of edge cases: 1x1 matrix, all firing, all non-firing, boundary cells
Trade-offs between clarity and optimization: copied matrix is simpler but uses O(mn) space; O(1) is complex but memory-efficient
Potential integer overflow when encoding if values are large, and how to mitigate (e.g., using modulo or bit shifts)

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