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Snapchat·Machine Learning Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Apr 2026

Summary

Snapchat ML engineer interview that came down to a classic graph problem. Nothing too exotic, but you better know your cycle detection cold.

Questions Asked (1)

Q1

Given a list of courses and their prerequisites, determine whether it's possible to complete all courses without getting stuck in a dependency cycle.

Algorithms & Data Structures
Author's notes

Classic directed graph problem dressed up as a scheduling puzzle.

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AI HintsAI Generated

Suggested Approach

Model the courses and prerequisites as a directed graph and check for cycles using either Kahn's algorithm (BFS topological sort) or DFS with recursion stack. If a topological ordering exists, all courses can be completed; otherwise, a cycle prevents completion.

Pro tip: Mention that this is essentially cycle detection in a directed graph, and that Kahn's algorithm is often preferred in interviews because it's iterative and avoids recursion depth issues. Also, briefly discuss how this applies to real ML pipelines where dependencies must be resolved.

1. Clarify and Model the Problem

Confirm that courses are nodes and prerequisites are directed edges (e.g., A -> B means A must be taken before B). Ask about input format and constraints.

2. Choose an Algorithm

Select either Kahn's algorithm (BFS topological sort) or DFS with cycle detection. Explain the trade-offs in terms of simplicity and recursion limits.

3. Implement the Solution

For Kahn's: compute in-degrees, use a queue, and count processed nodes. For DFS: track visited and recursion stack to detect back edges.

4. Analyze Complexity and Edge Cases

State time and space complexity (O(V+E)). Discuss edge cases like empty input, disconnected graphs, and self-loops.

5. Relate to ML Engineering

Connect to ML pipeline dependency resolution, ensuring no circular dependencies in training workflows or data processing steps.

Key Points to Mention

  • Directed graph representation: adjacency list for efficiency.
  • Cycle detection via topological sort (Kahn's algorithm) or DFS with recursion stack.
  • Time and space complexity: O(V+E) time, O(V+E) space.
  • Handling edge cases: empty input, disconnected components, self-loops.
  • Application to ML pipelines: ensuring acyclic dependencies in training workflows.
  • Trade-offs between BFS and DFS approaches in terms of implementation and memory.

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