Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026Remote

Summary

Meta coding round where the actual algorithm was the easy part. The whole challenge was just figuring out how to use their weird nested class structure to get the data you needed in the first place.

Questions Asked (1)

You're handed a codebase where a maze is wrapped in deeply nested OOP classes. The grid, walls, start, and end cells are only reachable through chained method calls on these objects. Implement BFS to find the shortest path from start to end.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

BFS itself took maybe five minutes to write.

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AI HintsAI Generated

Suggested Approach

First, decouple the maze data from the OOP wrapper by extracting the grid, walls, start, and end into simple data structures (e.g., 2D array, coordinate tuples). Then implement standard BFS on the extracted data, tracking visited cells and parent pointers to reconstruct the shortest path. Finally, map the path back to the original objects if needed, and discuss trade-offs of the extraction approach.

Pro tip: Mention that you would write a thin adapter layer to isolate the OOP interface, making the BFS testable and the code maintainable. This shows you think about long-term code health, not just solving the puzzle.

1. Understand the OOP interface

Explore the nested classes to identify methods that expose grid dimensions, wall checks, and start/end positions. Note any performance costs of chained calls.

2. Extract to a simple representation

Build a lightweight adapter or copy the maze into a 2D array and coordinate variables, avoiding repeated OOP traversals during BFS.

3. Implement BFS

Use a queue to explore neighbors level by level, marking visited cells and storing parent pointers to reconstruct the shortest path.

4. Reconstruct and return the path

Backtrack from the end cell using parent pointers to build the path, then map it back to the original OOP objects if required.

5. Discuss trade-offs and optimizations

Talk about time/space complexity, the cost of extraction, and alternatives like lazy evaluation or modifying the OOP classes directly.

Key Points to Mention

BFS guarantees shortest path in unweighted grids; explain why DFS or Dijkstra is unnecessary.
Use a queue (FIFO) and a visited set to avoid cycles and redundant work.
Track parent pointers or distances to reconstruct the path efficiently.
Decoupling logic from the OOP wrapper improves testability and performance.
Time complexity O(R*C) and space O(R*C) for the queue and visited set.
Consider edge cases: no path, start equals end, and large grids.

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