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MathWorks·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

MathWorks software engineer interview with a classic grid pathfinding problem. Pretty standard algorithmic round but they wanted the full package: verbal explanation, complexity analysis, and a working implementation.

Questions Asked (1)

Q1

Given an m×n grid where cells are either empty or walls, find the shortest path between a start cell and a target cell. You can move in four directions. Return the path length or -1 if no path exists. Explain your algorithm, its time and space complexity, and implement it in C++, Java, or JavaScript.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

BFS is the obvious move here since you want shortest path in an unweighted grid.

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AI HintsAI Generated

Suggested Approach

Use BFS to find the shortest path in an unweighted grid, as BFS guarantees the shortest path in terms of number of steps. Clearly explain the algorithm, its O(mn) time and space complexity, and then implement it in your chosen language with attention to edge cases and code clarity.

Pro tip: Mention that BFS is optimal for unweighted grids and discuss potential optimizations like bidirectional BFS or A* with Manhattan distance for large grids, showing awareness of trade-offs.

1. Clarify the problem

Restate the problem to ensure understanding: grid dimensions, movement allowed (4 directions), start and target cells, walls, and return value. Ask clarifying questions if needed.

2. Choose the algorithm

Select BFS because it finds the shortest path in an unweighted graph. Explain why BFS is appropriate and mention alternatives like DFS (not optimal) or A* (if heuristics available).

3. Explain the approach

Describe using a queue for BFS, a visited set or distance matrix, and exploring neighbors in four directions. Track distance from start to each cell.

4. Analyze complexity

State time complexity O(mn) since each cell is visited at most once, and space complexity O(mn) for the queue and visited structure in the worst case.

5. Implement and test

Write clean code in your chosen language, handling edge cases like start equals target, no path, or invalid inputs. Walk through a small example to verify.

Key Points to Mention

  • BFS guarantees shortest path in unweighted graphs
  • Time and space complexity are O(mn)
  • Use a queue for BFS and a visited set or distance matrix
  • Handle edge cases: start == target, no path, empty grid
  • Consider bidirectional BFS or A* for large grids as optimization
  • Code should be modular and readable with comments

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