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Meta·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate
Apr 2026

Summary

Meta SWE coding round, just the one question about maze pathfinding. Pretty standard graph traversal stuff, nothing that should trip you up if you've done any BFS or DFS prep.

Questions Asked (1)

Q1

Given a 2D grid where 0 is open and 1 is a wall, determine if there's a path from a start cell to a target cell using 4-directional movement.

Algorithms & Data Structures
Author's notes

Classic BFS/DFS maze problem.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose a graph traversal algorithm like BFS or DFS. Explain the algorithm step-by-step, analyze time and space complexity, and discuss potential optimizations or trade-offs.

Pro tip: Mention that BFS is generally preferred for finding the shortest path, but DFS is simpler and sufficient if only connectivity is required. Also, discuss how to handle large grids by using iterative approaches to avoid stack overflow.

1. Clarify the problem

Ask about grid dimensions, movement constraints, and whether diagonal moves are allowed. Confirm if the start and target are guaranteed to be open.

2. Choose an algorithm

Select BFS for shortest path or DFS for connectivity. Consider iterative implementations to avoid recursion depth issues.

3. Walk through the algorithm

Explain how to explore neighbors, mark visited cells, and terminate when the target is found or all reachable cells are exhausted.

4. Analyze complexity

State that time complexity is O(m*n) and space complexity is O(m*n) for the visited set and queue/stack.

5. Discuss edge cases and optimizations

Cover cases like start equals target, no path, or large grids. Mention in-place marking or bidirectional BFS for optimization.

Key Points to Mention

  • BFS vs DFS trade-offs
  • Time and space complexity analysis
  • Handling edge cases (e.g., start=target, no path)
  • Using a visited set to avoid cycles
  • Iterative vs recursive implementation
  • Potential optimizations like bidirectional search

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