Scale.ai·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate

Apr 2026

Summary

Scale.ai SWE coding round with a matrix simulation problem. The rules were different from the classic version but the code structure ended up being almost identical, which was either a relief or a little anticlimactic depending on how you look at it.

Questions Asked (1)

Given a 2D matrix where each cell can be in one of several states, apply a set of custom transition rules simultaneously to all cells and return the resulting matrix.

Algorithms & Data Structures

Author's notes

The problem was basically Game of Life with different rules swapped in.

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AI HintsAI Generated

Suggested Approach

Clarify the cell states and transition rules, then simulate the simultaneous update using a copy of the matrix or in-place encoding. Emphasize that all cells must be updated based on the original state, not intermediate changes.

Pro tip: Mention that in-place updates can be done by encoding both old and new states in a single integer (e.g., using bit manipulation) to save space, but only if the state space is small. Also, discuss how to handle edge cases like empty matrix or single row/column.

1. Understand the problem

Ask clarifying questions about the number of states, the exact transition rules, and whether the matrix is mutable. Confirm that updates are simultaneous.

2. Choose an approach

Decide between using an auxiliary matrix (simpler) or in-place encoding (more space-efficient). Consider time and space complexity trade-offs.

3. Implement the simulation

Iterate through each cell, compute its next state based on the original matrix, and store it either in the auxiliary matrix or encoded in-place.

4. Handle edge cases

Check for empty matrix, single row/column, and boundaries. Ensure the solution works for all possible state combinations.

5. Test and verify

Walk through a small example, verify simultaneous updates, and discuss potential optimizations or alternative approaches.

Key Points to Mention

Simultaneous update requirement and how to avoid using updated values
Time and space complexity analysis (O(m*n) time, O(m*n) or O(1) space)
In-place encoding technique using bit manipulation or state mapping
Edge cases: empty matrix, 1x1 matrix, all cells same state
Potential for parallelization or vectorization if applicable
Clarifying questions about the number of states and transition rules

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