Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026Remote

Summary

Meta SWE coding round, one algorithmic question about finding the kth largest element in an array. Pretty standard but they pushed on edge cases harder than I expected.

Questions Asked (1)

Given an unsorted array (which may include duplicates and negative numbers, up to 10^5 elements), find the kth largest element. Walk through your algorithm, analyze the time complexity, and provide at least three test cases. Also handle the case where k exceeds the array length.

Algorithms & Data Structures

Author's notes

I went with a min-heap of size k, which gets you O(n log k) and I felt pretty good about that.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then propose an efficient solution using a min-heap of size k or Quickselect. Walk through the algorithm step-by-step, analyze time and space complexity, and provide test cases including duplicates, negatives, and k out of bounds.

Pro tip: Mention that Quickselect has average O(n) but worst-case O(n^2), and you can use a randomized pivot or Median of Medians to guarantee O(n). Also, discuss the trade-offs between heap and Quickselect based on k and memory constraints.

1. Clarify requirements and edge cases

Ask about input size, value range, whether k is 1-indexed, and how to handle k > array length. Confirm expected output for invalid k (e.g., return null or throw exception).

2. Choose an algorithm

Decide between sorting, min-heap, or Quickselect. For large n and small k, min-heap is efficient; for average O(n), Quickselect is preferred. Explain your choice based on constraints.

3. Walk through the algorithm

Describe step-by-step how the chosen method works. For min-heap: build heap of first k elements, then for each remaining element, if larger than heap root, replace and heapify. For Quickselect: partition around a pivot, recurse on the side containing the kth largest.

4. Analyze complexity

State time and space complexity. For min-heap: O(n log k) time, O(k) space. For Quickselect: average O(n), worst O(n^2), O(1) space if iterative. Mention randomization to avoid worst-case.

5. Provide test cases

Give at least three test cases: (1) array with duplicates and negatives, (2) k = 1 (largest), (3) k = array length (smallest), (4) k > length (invalid). For each, state expected output and why.

Key Points to Mention

Handling duplicates: ensure algorithm correctly counts duplicates as separate elements.
Negative numbers: algorithm should work regardless of sign.
k out of bounds: explicitly check and handle (e.g., return null or throw IllegalArgumentException).
Time complexity trade-offs: O(n log k) vs O(n) average, and when to use which.
Space complexity: O(k) for heap, O(1) for Quickselect.
Randomized pivot or Median of Medians to avoid worst-case O(n^2) in Quickselect.

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