Snapchat·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026

Summary

Snapchat ML engineer interview that was pretty graph-heavy. One coding problem, focused on connected components, with a follow-up pushing toward Union-Find specifically. Felt like they had a particular solution in mind from the start.

Questions Asked (1)

Given an undirected graph as a list of nodes and edges, find all connected components and partition the nodes into groups accordingly. Follow-up: implement this using Union-Find with path compression and union by rank.

Algorithms & Data Structures

Author's notes

Started with BFS which felt natural to me, got through it fine, but then they asked me to redo it with Union-Find.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the graph representation and constraints, then present a straightforward solution using BFS/DFS to find connected components. For the follow-up, implement Union-Find with path compression and union by rank, explaining how these optimizations improve efficiency. Finally, analyze time and space complexity and discuss trade-offs.

Pro tip: Mention that Union-Find is particularly useful for dynamic connectivity and can be extended to handle large-scale graphs in ML pipelines, such as clustering user sessions on Snapchat. Also, emphasize the near-constant time per operation with path compression and union by rank.

1. Clarify the problem

Ask about input format (adjacency list/matrix), graph size, and whether the graph is static or dynamic. Confirm that nodes are labeled 0 to n-1 for simplicity.

2. Present baseline solution

Describe using BFS or DFS to traverse the graph, marking visited nodes and collecting components. Mention time complexity O(V+E) and space O(V).

3. Introduce Union-Find

Explain the Union-Find data structure with parent and rank arrays. Describe find with path compression and union by rank, and how to initialize each node as its own set.

4. Implement Union-Find

Walk through the algorithm: for each edge, union the two nodes. After processing all edges, group nodes by their root parent to form components.

5. Analyze and discuss

State that Union-Find with optimizations runs in nearly O(E α(V)) time, where α is the inverse Ackermann function. Compare with BFS/DFS and mention use cases.

Key Points to Mention

Graph representation: adjacency list vs. adjacency matrix and their impact on complexity.
BFS/DFS approach: using a visited set and iterating over all nodes to find components.
Union-Find operations: find with path compression, union by rank/size, and their effect on time complexity.
Time complexity: O(V+E) for BFS/DFS, O(E α(V)) for Union-Find with optimizations.
Space complexity: O(V) for both approaches.
Real-world application: connected components in social networks (e.g., Snapchat user groups) and ML clustering.

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