Uber·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Uber SWE interview, coding round, one meaty grid simulation problem that took up most of the time. Not super brutal but there were enough layers to trip you up if you weren't careful.

Questions Asked (1)

A robot starts at (0,0) on an infinite integer grid and receives a sequence of movement commands (U, D, L, R). Some cells are blocked. If a move would land on a blocked cell, the robot stays put. Return the maximum squared Euclidean distance from the origin at any point during the simulation. Handle up to 1e5 commands and 1e5 obstacles efficiently.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went straight for the hash set on obstacles, which was the right call, O(1) lookup per move.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose an efficient simulation using a hash set for obstacles and tracking the robot's position. Optimize by precomputing obstacle lookups and updating the maximum squared distance only when the position changes.

Pro tip: Mention that you can avoid checking obstacles for every move by storing them in a hash set, and that you can early-exit if the maximum possible distance is reached, though it's not necessary for the given constraints.

1. Clarify requirements and constraints

Confirm the grid is infinite, obstacles are given as coordinates, and the robot stays put if a move is blocked. Ask about input format and whether obstacles can be at the origin.

2. Choose data structures

Use a hash set to store obstacle coordinates for O(1) lookup. Maintain the robot's current position as (x, y) and the maximum squared distance.

3. Simulate movements

For each command, compute the next position. If it's not blocked, update the position; otherwise, stay. After each move (or attempted move), update the maximum squared distance if the current distance is larger.

4. Analyze complexity and edge cases

Time complexity is O(N + M) where N is commands and M is obstacles. Space is O(M). Discuss edge cases: no obstacles, all moves blocked, obstacles at origin, large coordinates.

5. Test with examples

Walk through a small example to verify correctness, such as commands 'UR' with an obstacle at (1,0).

Key Points to Mention

Use a hash set for O(1) obstacle lookup
Track current position and update max squared distance efficiently
Handle blocked moves by not updating position
Consider edge cases like obstacles at origin or no obstacles
Time complexity O(N + M) and space O(M)
Potential optimization: early termination if max possible distance reached (optional)

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