LinkedIn·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

LinkedIn SWE interview with a classic array problem. Nothing too surprising but the follow-up on time complexity is where things get interesting.

Questions Asked (1)

Given an integer array and an integer k, find the kth largest element in the array. The kth largest is based on sorted order, not distinct values.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went with the min-heap approach, keep a heap of size k and just push everything through it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., array size, value range, memory limits) and then present multiple solutions with trade-offs: sorting, min-heap, and Quickselect. Emphasize the optimal average-case O(n) Quickselect approach while noting its worst-case O(n^2) and how to mitigate it with random pivot selection.

Pro tip: Mention that Quickselect's worst-case can be avoided with randomized pivoting or the Median of Medians algorithm, showing awareness of practical robustness. Also, discuss how the choice depends on whether the array is static or dynamic (e.g., streaming data).

1. Clarify requirements and constraints

Ask about input size, value range, memory limits, and whether the array can be modified. This determines the suitable algorithm.

2. Propose a baseline solution

Suggest sorting the array and picking the element at index n-k. This is simple but O(n log n) time and may be inefficient for large n.

3. Present optimized approaches

Introduce a min-heap of size k for O(n log k) time, and Quickselect for average O(n) time. Explain the trade-offs between time, space, and worst-case performance.

4. Discuss implementation details and edge cases

Cover pivot selection (randomized), handling duplicates, and edge cases like k=1, k=n, or empty array. Mention that Quickselect modifies the array.

5. Conclude with the best choice

Recommend Quickselect for average-case efficiency, but note that a heap is preferable if the array is streamed or k is small. Summarize the trade-offs.

Key Points to Mention

Time and space complexity of each approach: sorting O(n log n), heap O(n log k), Quickselect average O(n) worst O(n^2).
Quickselect algorithm details: partition around a pivot, recurse on one side, and use random pivot to avoid worst-case.
Heap approach: maintain a min-heap of size k, iterate through array, and the root will be the kth largest.
Handling duplicates: the kth largest is based on sorted order, so duplicates count as separate elements.
Edge cases: k=1 (maximum), k=n (minimum), empty array, and k out of bounds.
Trade-offs: Quickselect modifies input and has worst-case risk; heap is stable and works well for streaming data.

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