LinkedIn·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

LinkedIn SWE interview that came down to a classic dynamic programming problem. Nothing too exotic, but they wanted you to know more than one way to solve it.

Questions Asked (1)

Given an integer array, find the contiguous subarray with the largest sum and return that sum. Be prepared to walk through both a linear-time solution and a divide-and-conquer approach.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I knew Kadane's cold so that part was fine.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then present Kadane's algorithm for the optimal O(n) solution. Follow up with the divide-and-conquer approach, explaining its O(n log n) time complexity and when it might be preferred. Conclude by comparing trade-offs and discussing potential optimizations.

Pro tip: Mention that Kadane's algorithm can be adapted to return the subarray indices, and note that the divide-and-conquer approach is a good example of the merge sort paradigm, which LinkedIn values for its scalability insights.

1. Clarify requirements and edge cases

Ask about input constraints (e.g., array size, possible values) and handle edge cases like empty array or all negative numbers. Confirm whether the subarray must be non-empty.

2. Present the linear-time solution (Kadane's algorithm)

Explain the idea of maintaining a running maximum sum and updating the global maximum. Walk through a small example to illustrate.

3. Present the divide-and-conquer approach

Describe splitting the array into halves, recursively finding the maximum subarray sum in each half, and then finding the maximum crossing sum. Combine results to get the overall maximum.

4. Compare time and space complexity

State that Kadane's algorithm runs in O(n) time and O(1) space, while divide-and-conquer runs in O(n log n) time and O(log n) space due to recursion. Discuss when each might be appropriate.

5. Discuss trade-offs and potential optimizations

Mention that Kadane's is optimal for this problem, but divide-and-conquer can be parallelized or used if the array is distributed. Also note that Kadane's can be modified to return the subarray.

Key Points to Mention

Kadane's algorithm: dynamic programming approach with O(n) time and O(1) space.
Divide-and-conquer: O(n log n) time, O(log n) space, based on splitting and merging.
Handling all-negative arrays: Kadane's algorithm works if initialized properly.
Crossing sum calculation in divide-and-conquer: compute max suffix of left half and max prefix of right half.
Trade-offs: linear is faster but divide-and-conquer is more parallelizable.
Edge cases: empty array, single element, all negative numbers.

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