Snowflake·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026Remote

Summary

Snowflake technical phone screen with a grid simulation problem. Pretty niche question, not your typical BFS/DFS setup, and the edge cases are where it gets you.

Questions Asked (1)

You're given an n x m grid where a walker moves right one cell at a time, wrapping to the next row when it hits the last column. Some cells are obstacles, some are teleporters. Return the number of steps to reach the bottom-right cell, or -1 if an obstacle is hit, or -2 if the walker enters an infinite loop.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The movement rule threw me off at first because it's not standard 2D traversal.

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AI HintsAI Generated

Suggested Approach

Model the walker's movement as a deterministic state machine where each state is a cell (row, col). Simulate the walk step-by-step, using a visited set to detect cycles, and handle obstacles and teleporters as special transitions. Return the step count when reaching the bottom-right, -1 if an obstacle is hit, or -2 if a state repeats.

Pro tip: Clarify with the interviewer whether teleporters are one-way or two-way, and whether they can lead to obstacles or out-of-bounds; these edge cases often determine correctness.

1. Clarify rules and edge cases

Ask about teleporter behavior (destination, one-way/two-way), obstacle handling, and what happens if the start cell is an obstacle or the target is unreachable.

2. Define state and transitions

Represent each cell as a state (row, col). Define the next state: if current cell is a teleporter, jump to its destination; otherwise, move right with wrap-around to the next row.

3. Simulate with cycle detection

Iterate step-by-step, maintaining a visited set of states. If the next cell is an obstacle, return -1; if the next state is already visited, return -2; if the target is reached, return the step count.

4. Analyze complexity and trade-offs

Discuss time and space complexity: O(n*m) time and space in the worst case. Mention alternative approaches like graph cycle detection (e.g., Floyd's algorithm) and their trade-offs.

5. Test with examples

Walk through small grids: no obstacles, obstacle blocking, teleporter causing loop, teleporter skipping to target, and wrap-around behavior.

Key Points to Mention

State representation: each cell is a unique state; teleporters change the transition function.
Cycle detection: use a visited set or Floyd's tortoise and hare to detect infinite loops.
Obstacle handling: check before moving; if the next cell is an obstacle, return -1 immediately.
Wrap-around logic: when moving right from the last column, go to column 0 of the next row.
Edge cases: start cell is obstacle, target is obstacle, teleporter leads to obstacle, teleporter leads out of bounds.
Complexity: O(n*m) time and space; can optimize space with Floyd's algorithm if needed.

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