Meta·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Meta SWE interview that went deep on graph traversal, specifically a BFS variant I hadn't fully thought through before. The keys-and-doors twist sounds like a minor extension until you realize the whole visited-state logic has to change.

Questions Asked (1)

Given a grid where lowercase letters are keys and uppercase letters are locked doors, extend a standard BFS to handle collecting keys and unlocking doors. How do you define visited state, and what are the tradeoffs as the number of keys grows?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was to just track (row, col) like normal BFS and I immediately started going down that path before catching myself.

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AI HintsAI Generated

Suggested Approach

Start by explaining the standard BFS on a grid and then describe how to augment the state with a bitmask of collected keys. Discuss how the visited set must track both position and key set, and analyze the exponential growth in state space as keys increase.

Pro tip: Mention that you can optimize by only tracking keys that are actually needed to reach the target, or by using a BFS with state compression, but always be prepared to discuss the trade-off between memory and time.

1. Define the state

Explain that each state in BFS is a tuple (row, col, keys_bitmask), where keys_bitmask represents the set of keys collected so far.

2. Handle transitions

Describe how to move to adjacent cells: if it's a key, update the bitmask; if it's a door, only proceed if the corresponding key bit is set; otherwise, move freely.

3. Track visited states

Use a visited set or 3D array to mark (row, col, keys_bitmask) as visited to avoid revisiting the same state with the same key set.

4. Analyze complexity

Discuss that the state space is O(R*C*2^K), where K is the number of keys, leading to exponential growth in time and memory as K increases.

5. Discuss trade-offs and optimizations

Mention that while the bitmask approach is straightforward, it becomes impractical for large K; possible optimizations include only tracking keys that are relevant to the goal or using bidirectional BFS.

Key Points to Mention

State representation: (row, col, keys_bitmask)
Bitmask operations for adding keys and checking doors
Visited set must include key set to avoid missing optimal paths
Time and space complexity: O(R*C*2^K)
Exponential blowup as number of keys grows
Potential optimizations: pruning irrelevant keys, bidirectional search, or A* with admissible heuristic

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