Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Meta SWE interview where they handed me a broken maze solver and told me to fix it with AI tooling. Basically a debugging exercise on an unfamiliar codebase, grid-based maze with walls, a start, and an end point. Straightforward premise but the pressure of reading someone else's code fast is real.

Questions Asked (1)

You're given a buggy maze solver codebase. The maze is represented as a grid with walls, a start point S, and an end point E. Some tests are failing. Use an AI coding assistant to read the code, figure out what's broken, and fix it.

Algorithms & Data StructuresRoot Cause Analysis

Author's notes

The tricky part wasn't finding the bug, it was orienting myself in code I'd never seen before.

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AI HintsAI Generated

Suggested Approach

First, run the failing tests to understand the expected vs actual behavior, then use the AI assistant to trace the code path and identify the bug. After fixing, re-run tests and add edge cases to ensure robustness.

Pro tip: Treat the AI as a pair programmer: ask it to explain the code's logic and suggest hypotheses, but always verify its fixes with tests and your own reasoning.

1. Reproduce and Understand the Failure

Run the test suite to see which tests fail and what the expected output is. Read the failing test cases to understand the maze setup and the solver's incorrect behavior.

2. Analyze the Code with AI Assistance

Use the AI assistant to summarize the maze solver's algorithm and identify potential issues. Ask it to explain the traversal logic, boundary checks, and path reconstruction.

3. Formulate and Test Hypotheses

Based on the analysis, hypothesize the root cause (e.g., off-by-one error, incorrect neighbor iteration, missing visited check). Use the AI to suggest fixes, but validate each with targeted test runs.

4. Implement and Verify the Fix

Apply the fix, ensuring it addresses the root cause without breaking other functionality. Re-run all tests, including edge cases like no path, start equals end, and large mazes.

5. Reflect and Communicate

Summarize the bug, the fix, and how you used the AI assistant effectively. Highlight any lessons learned about debugging or the algorithm.

Key Points to Mention

Systematic debugging: reproduce, isolate, hypothesize, test, fix
Common maze solver bugs: boundary conditions, visited set management, incorrect neighbor directions
Effective use of AI: asking for explanations, generating test cases, suggesting fixes, but verifying independently
Algorithm choice: BFS vs DFS and their implications for shortest path and memory
Testing: unit tests, edge cases (no path, start=end, empty maze), and performance considerations
Communication: explaining the root cause and fix clearly, and acknowledging AI's role

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