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Snapchat·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Jun 2026Remote

Summary

Snapchat ML engineer interview that was heavier on algorithms than I expected. The grid traversal problem was manageable but the follow-up with two users caught me off guard and I had to think out loud for a while before landing on something reasonable.

Questions Asked (2)

Q1

You have an m×n grid where walls (1), empty cells (0), and meeting rooms (2) are marked. Given a starting position, find the coordinates of the nearest reachable meeting room using unit-cost moves in four directions. Return [-1, -1] if none are reachable. Walk through your algorithm and its time and space complexity.

Algorithms & Data Structures
Author's notes

BFS was the obvious call here and I got there pretty fast.

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AI HintsAI Generated

Suggested Approach

Model the grid as a graph and use BFS from the starting position to find the nearest meeting room, as BFS guarantees the shortest path in an unweighted grid. Early exit when the first meeting room is dequeued, and return [-1, -1] if the queue empties without finding one.

Pro tip: Mention that BFS is optimal here because all moves have equal cost, and note that early termination can significantly reduce runtime in practice. Also, clarify that you handle edge cases like the start being a meeting room or an invalid position.

1. Clarify the problem and constraints

Confirm the grid dimensions, movement directions, and what constitutes a valid move. Ask about edge cases such as starting on a wall or meeting room, and whether the grid can be modified.

2. Choose BFS as the algorithm

Explain that BFS is ideal for finding the shortest path in an unweighted grid. Justify why DFS or Dijkstra would be less efficient or unnecessary.

3. Outline the BFS implementation

Describe initializing a queue with the start position, a visited set to avoid revisiting cells, and processing neighbors in four directions. Check for meeting rooms upon dequeue or enqueue.

4. Analyze time and space complexity

State that time complexity is O(m*n) since each cell is visited at most once, and space complexity is O(m*n) for the queue and visited set in the worst case.

5. Discuss optimizations and edge cases

Mention early termination when a meeting room is found, and handling cases where no meeting room is reachable. Optionally, discuss bidirectional BFS or multi-source BFS if multiple starts are considered.

Key Points to Mention

  • BFS guarantees shortest path in unweighted graphs
  • Use a queue for level-order traversal
  • Track visited cells to avoid cycles and redundant work
  • Check for meeting room when dequeuing or enqueuing
  • Time complexity O(m*n), space complexity O(m*n)
  • Early exit when the first meeting room is found

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

Q2

Follow-up: given two users starting at different positions, find the meeting room that minimizes the combined shortest-path distance for both, considering only rooms both users can actually reach. Return the room coordinates and the minimum total distance.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This one took me a minute to structure.

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AI HintsAI Generated

Suggested Approach

Model the environment as a graph where rooms are nodes and connections are edges, then run BFS from each user to compute shortest distances to all reachable rooms. Iterate over the intersection of reachable rooms to find the one minimizing the sum of distances, returning its coordinates and the total distance.

Pro tip: Clarify upfront whether the graph is unweighted (BFS) or weighted (Dijkstra), and mention that if the graph is large but queries are frequent, precomputing all-pairs shortest paths or using landmarks can be beneficial.

1. Clarify assumptions and input format

Confirm whether the graph is unweighted or weighted, directed or undirected, and how rooms/coordinates are represented. Ask about constraints (e.g., number of rooms, edges) to choose the right algorithm.

2. Compute shortest distances from each user

Run BFS (for unweighted) or Dijkstra (for weighted) from each user's starting position to compute distances to all reachable rooms. Store distances in hash maps keyed by room ID.

3. Find common reachable rooms

Take the intersection of the two distance maps to get rooms reachable by both users. If the intersection is empty, return no meeting room.

4. Minimize combined distance

Iterate over the common rooms, compute the sum of distances for each, and track the room with the minimum total. Return its coordinates and the minimum sum.

5. Analyze complexity and discuss optimizations

State time complexity O(V+E) for BFS per user, plus O(V) for intersection and minimization. Mention potential optimizations like bidirectional BFS or precomputation for multiple queries.

Key Points to Mention

  • Graph representation: adjacency list for sparse graphs, adjacency matrix for dense graphs.
  • BFS for unweighted graphs, Dijkstra for weighted graphs (or 0-1 BFS if weights are 0/1).
  • Handling unreachable rooms: only consider rooms present in both distance maps.
  • Edge cases: no common reachable room, start and end same, disconnected graph.
  • Time and space complexity: O(V+E) per BFS, O(V) space for distance maps.
  • Trade-offs: precomputing all-pairs shortest paths vs. on-the-fly BFS for multiple queries.

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