Meta·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Meta SWE interview with a maze exploration problem that's basically BFS but wrapped in a weird API abstraction layer. The follow-up on edge cases and unit tests was where things got interesting.

Questions Asked (2)

You're given a black-box 2D maze interface with only local actions: move in a direction (returns success/fail), get current position, and check if you've found the cheese. Design an algorithm to reach the cheese in the fewest steps, minimizing redundant moves. Cover visited-state tracking, frontier management, termination, and complexity relative to reachable cells.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was to just say BFS and call it a day, but the tricky part is you can't teleport.

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AI HintsAI Generated

Suggested Approach

Treat the maze as an unknown graph and use BFS to explore it, since BFS guarantees the shortest path in terms of steps. Maintain a visited set and a frontier queue, and simulate moves using the black-box interface. Analyze complexity in terms of reachable cells, not total grid size.

Pro tip: Emphasize that BFS is optimal for unweighted graphs and that you avoid redundant moves by not revisiting cells. Also mention that you can stop as soon as the cheese is found, and that the algorithm is complete and optimal.

1. Model the maze as a graph

Represent each cell as a node and possible moves as edges. Since the maze is unknown, you discover nodes and edges as you explore.

2. Choose BFS for shortest path

Use BFS because it finds the shortest path in an unweighted graph. Maintain a queue for the frontier and a set for visited cells to avoid cycles.

3. Simulate moves and track state

From the current cell, try each direction; if the move succeeds, record the new cell and its parent. Use the black-box actions to get position and check for cheese.

4. Terminate when cheese is found

Stop as soon as the cheese is detected. Reconstruct the path from start to cheese using parent pointers if needed.

5. Analyze complexity

Time and space are O(V+E) where V is reachable cells and E is traversable edges. This is optimal for exploration without prior knowledge.

Key Points to Mention

BFS guarantees shortest path in unweighted graphs.
Visited set prevents redundant moves and cycles.
Frontier queue manages exploration order.
Termination condition: cheese found.
Complexity relative to reachable cells, not total grid size.
Path reconstruction using parent pointers if needed.

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

Propose unit tests for the maze exploration algorithm covering: obstacles at the start position, multiple cheeses, unreachable cheese, mazes with loops, unknown/unbounded grid size, and large open areas.

Algorithms & Data StructuresAdaptability & Ambiguity

Author's notes

This part I actually felt better about.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the algorithm's contract and edge cases, then systematically design tests for each scenario, prioritizing correctness and termination. Use a mix of unit tests with mocked grids and property-based tests for unbounded or large inputs.

Pro tip: Emphasize test isolation and determinism: use dependency injection for the grid and random seed control to avoid flaky tests, and always assert on both the result and the algorithm's behavior (e.g., visited cells).

1. Clarify requirements and assumptions

Ask about the algorithm's expected behavior: return type, handling of unreachable cheese, and whether the grid is finite or infinite. Confirm if the algorithm should modify the grid or use a separate visited set.

2. Design tests for each scenario

For each given scenario, outline specific test cases: e.g., start on obstacle (expect immediate failure or exception), multiple cheeses (expect all reachable collected), unreachable cheese (expect partial collection or specific error).

3. Incorporate edge cases and properties

Add tests for loops (ensure no infinite loop), unbounded grid (use lazy generation or mock), and large open areas (performance and memory). Include property-based tests for invariants like 'all reachable cheeses are collected'.

4. Structure tests for maintainability

Organize tests using a testing framework (e.g., JUnit, pytest) with clear naming and setup/teardown. Use parameterized tests for different grid configurations and mocks for external dependencies.

5. Discuss coverage and tooling

Mention how to measure coverage (e.g., branch coverage) and use mutation testing to validate test effectiveness. Suggest fuzzing for unbounded scenarios.

Key Points to Mention

Test for start position on obstacle: expect exception or immediate return with error.
Multiple cheeses: verify all reachable cheeses are collected and count matches.
Unreachable cheese: ensure algorithm terminates and reports unreachable or returns partial result.
Mazes with loops: confirm no infinite recursion/iteration and correct pathfinding.
Unknown/unbounded grid: use lazy evaluation or mock to simulate infinite grid and test termination.
Large open areas: test performance and memory usage, possibly with timeouts or resource limits.

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