Uber·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Uber coding round for a Software Engineer role. One algorithmic problem, simulation-heavy, not the kind of thing you can brute force your way through in 45 minutes without thinking carefully about the traversal logic first.

Questions Asked (1)

You have two arrays and a starting energy value K. For each possible start index, simulate moving forward through the layers: at each step you subtract the current layer's cost from your energy, and if energy goes negative you stop immediately. If your remaining energy meets or exceeds a threshold for that layer, you score a point and keep going, otherwise you stop. Return an array where each entry is the max points achievable starting from that index.

Algorithms & Data Structures

Author's notes

The simulation part clicked fast but I kept second-guessing the termination conditions.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem details and constraints (array sizes, energy values, threshold definition). Then, propose a solution that precomputes prefix sums of costs and thresholds to enable efficient simulation for each start index, possibly using binary search or a sliding window to find the maximum points. Discuss time and space complexity trade-offs.

Pro tip: Mention that you would handle edge cases like K being less than the first cost or threshold, and that you would test with small examples to verify the logic before coding.

1. Clarify the problem

Ask questions to confirm the meaning of 'layers', how thresholds are defined, and whether arrays are 1D or 2D. Ensure you understand the stopping conditions and scoring.

2. Identify constraints and edge cases

Determine array sizes, energy range, and threshold values. Consider cases where K is too small to score any points, or where the arrays are empty.

3. Design an efficient algorithm

Use prefix sums to quickly compute cumulative costs and thresholds. For each start index, simulate forward but optimize by precomputing the maximum reachable index using binary search or two pointers.

4. Analyze complexity

State the time and space complexity of your approach. Aim for O(n log n) or O(n) if possible, and explain why it's optimal.

5. Test with examples

Walk through a small example to verify correctness, including edge cases. Discuss potential pitfalls and how to handle them.

Key Points to Mention

Prefix sums for cumulative costs and thresholds
Binary search or two-pointer technique to find the stopping point
Time complexity: O(n log n) or O(n) with optimization
Space complexity: O(n) for prefix arrays
Handling negative energy and threshold conditions
Edge cases: empty arrays, K insufficient, thresholds larger than costs

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