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Weride·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026Remote

Summary

Second round at Weride for a software engineer role, conducted with someone on the US team. Split pretty evenly between walking through my resume and a coding problem.

Questions Asked (1)

Q1

Solve the 'Swim in Rising Water' graph traversal problem (LeetCode 778).

Algorithms & Data Structures
Author's notes

This one is tricky if you haven't seen it before.

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AI HintsAI Generated

Suggested Approach

Model the grid as a graph where each cell's elevation is the cost to enter it, and the goal is to find a path from (0,0) to (n-1,n-1) that minimizes the maximum elevation along the path. Use either a modified Dijkstra's algorithm with a priority queue or binary search on the answer combined with BFS/DFS to check feasibility.

Pro tip: Clarify that the problem is equivalent to finding the minimax path, and mention that both Dijkstra and binary search+BFS have the same time complexity O(n^2 log n), but Dijkstra is often simpler to implement and explain.

1. Understand the problem

Restate the problem: find the minimum time t such that there is a path from top-left to bottom-right where all cells have elevation ≤ t. Note that you can wait, but waiting doesn't help because you can always start later.

2. Choose an approach

Decide between Dijkstra-like search (minimax path) or binary search on t with BFS/DFS feasibility check. Both are valid; pick the one you can implement most cleanly under pressure.

3. Implement the algorithm

For Dijkstra: use a min-heap storing (max_elevation_so_far, row, col), and update when a smaller max elevation is found. For binary search: define check(t) that runs BFS from start to end using only cells with elevation ≤ t.

4. Analyze complexity and edge cases

Time complexity: O(n^2 log n) for both approaches. Space: O(n^2). Handle edge cases: n=1 (return grid[0][0]), and ensure you don't revisit cells unnecessarily.

5. Test and optimize

Walk through a small example to verify correctness. Mention that you can optimize binary search bounds to [max(grid[0][0], grid[n-1][n-1]), max(grid)] or use a union-find approach if asked for alternatives.

Key Points to Mention

  • The problem is equivalent to finding a path that minimizes the maximum elevation along the path (minimax path).
  • Dijkstra's algorithm can be adapted by using the maximum elevation so far as the distance metric.
  • Binary search on the answer t, with BFS/DFS to check if a path exists using only cells with elevation ≤ t.
  • Time complexity: O(n^2 log n) for both approaches; space complexity: O(n^2).
  • Edge case: when n=1, the answer is simply grid[0][0].
  • Alternative approach: union-find by sorting cells by elevation and adding them incrementally until start and end are connected.

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