Amazon·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Amazon SWE coding round, one meaty algorithmic problem that took up most of the session. The question had a few layers to it and they wanted you to walk through tradeoffs, not just code a solution.

Questions Asked (1)

Given two integer arrays, find the length of the longest contiguous subarray that appears in both. If multiple exist, return any one pair of starting indices. Walk through different approaches and their tradeoffs in time and space complexity.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This one took me a while to even get my footing.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then propose a brute-force solution and progressively optimize using dynamic programming or binary search with hashing. Compare time and space complexities of each approach and discuss trade-offs, finally providing code for the optimal solution.

Pro tip: Amazon values customer obsession and ownership; relate the problem to real-world scenarios like finding common patterns in user behavior logs, and always discuss scalability and edge cases.

1. Clarify requirements and constraints

Ask about input size, value ranges, and whether the subarrays must be contiguous in both arrays. Confirm if returning any one pair of indices is acceptable.

2. Discuss brute-force approach

Explain checking all possible subarrays of one array and searching in the other, with O(n^3) or O(n^2) time complexity, and note its inefficiency for large inputs.

3. Propose optimized approaches

Describe dynamic programming (O(n*m) time and space) and binary search with rolling hash (O((n+m) log(min(n,m))) time). Highlight trade-offs between time and space.

4. Select and implement optimal solution

Choose the approach that best balances time and space for the given constraints. Walk through the algorithm and provide clean, efficient code.

5. Analyze complexity and edge cases

State the final time and space complexity, and discuss edge cases like empty arrays, no common subarray, and multiple longest subarrays.

Key Points to Mention

Time and space complexity of each approach (brute-force, DP, binary search + hashing)
Trade-offs between time and space, and when to choose which approach
Handling edge cases: empty arrays, no common subarray, multiple valid answers
Use of hashing to compare subarrays efficiently
Potential for further optimization using suffix automaton or suffix array
Real-world applications and scalability considerations

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