Snapchat·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Snapchat SWE interview that leaned hard into graph/tree problems. The main challenge was a BFS/DFS hybrid question with a follow-up on squeezing space complexity down, which I did not see coming.

Questions Asked (1)

Solve a graph or tree traversal problem using a combination of BFS and DFS, then explain how you would optimize space from O(N) to O(1) by reusing the input structure or marking visited states in-place.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I got the BFS/DFS part working fine but then they asked me to drop the visited set entirely and encode state directly into the input.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and choosing a concrete traversal example that naturally combines BFS and DFS. Then walk through the O(N) solution, identify where extra space is used, and explain how to eliminate it by reusing the input structure or marking visited states in-place. Finally, discuss trade-offs and edge cases.

Pro tip: Explicitly state that in-place marking changes the input and may require restoring it or using a sentinel value; this shows you consider production constraints and data integrity.

1. Clarify the problem and constraints

Ask about input size, mutability, and whether the graph/tree can be modified. Confirm the expected output and any space/time constraints.

2. Design a combined BFS+DFS approach

Explain how BFS and DFS work together, e.g., BFS for level-order exploration and DFS for deep path checks, and outline the O(N) space solution.

3. Identify space bottlenecks

Point out that the visited set or queue/stack contributes O(N) space. Discuss which structures can be eliminated or repurposed.

4. Optimize to O(1) space

Describe in-place marking techniques: for trees, use null pointers or modify node values; for graphs, use the adjacency matrix or swap elements. Explain how to detect visited states without extra memory.

5. Analyze trade-offs and edge cases

Discuss the impact on input data, potential need for restoration, and how the approach handles cycles, disconnected components, and large inputs.

Key Points to Mention

Difference between BFS and DFS and when to combine them (e.g., BFS for shortest path, DFS for backtracking).
Space complexity analysis: O(N) from visited set/queue/stack and how to reduce it.
In-place marking techniques: using sentinel values, modifying pointers, or reusing matrix cells.
Trade-offs: mutating input may not be allowed; need to restore state or use a copy if required.
Edge cases: cycles, self-loops, disconnected graphs, and large inputs that may cause stack overflow.
Time complexity remains O(N) or O(N+E) even with space optimization.

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