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Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Google SWE coding round, one question the whole time and it was a graph problem dressed up in grid clothing. Felt pretty solid on the approach but took me a minute to see the connection to a classic problem.

Questions Asked (1)

Q1

Given a 2D grid of integers, find a path from the top-left cell to the bottom-right cell (moving in four directions) that minimizes the maximum cell value encountered along the way. Return that minimum possible maximum value.

Algorithms & Data Structures
Author's notes

I knew BFS was involved but my first instinct was to binary search on the answer and check reachability at each candidate value.

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AI HintsAI Generated

Suggested Approach

Reframe the problem as a decision problem: can we reach the target with all cell values ≤ X? Then binary search on X and use BFS/DFS to check connectivity. Alternatively, use a priority queue (Dijkstra-like) to always expand the cell with the smallest maximum value so far.

Pro tip: Mention that this is a minimax path problem and can be solved optimally with a modified Dijkstra or binary search + BFS; also note that if the grid is small, a simple BFS with a threshold works, but for large grids, the priority queue approach is more efficient.

1. Clarify and Restate

Confirm the problem: find a path from (0,0) to (n-1,m-1) moving up/down/left/right that minimizes the maximum value along the path. Ask about constraints (grid size, value range) to choose the best algorithm.

2. Identify the Core Technique

Recognize this as a minimax path problem. Two common approaches: (1) binary search on the answer and check feasibility with BFS/DFS, or (2) use a priority queue to always expand the cell with the smallest maximum value so far (Dijkstra-like).

3. Outline the Algorithm

For binary search: set low = max(grid[0][0], grid[n-1][m-1]), high = max value in grid. While low < high, mid = (low+high)/2, check if a path exists using only cells ≤ mid. If yes, high = mid; else low = mid+1. For priority queue: initialize a min-heap with (grid[0][0], 0, 0) and a visited set. While heap not empty, pop the cell with smallest max value; if it's the target, return that value; else push neighbors with max(current_max, neighbor_value).

4. Analyze Complexity

Binary search + BFS: O(NM log(maxVal)) time, O(NM) space. Priority queue: O(NM log(NM)) time, O(NM) space. Mention that both are efficient for typical grid sizes.

5. Discuss Edge Cases and Trade-offs

Handle single-cell grid (return its value), grids with all equal values, and unreachable paths (though problem guarantees a path). Compare approaches: binary search is simpler to implement, priority queue may be faster if the answer is small.

Key Points to Mention

  • Minimax path problem and its relation to bottleneck shortest path
  • Binary search on the answer with feasibility check via BFS/DFS
  • Dijkstra-like approach using a priority queue to track the maximum value along the path
  • Time and space complexity analysis for both approaches
  • Edge cases: single cell, all equal values, large value ranges
  • Trade-offs: simplicity vs. potential performance gains

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