Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Meta SWE coding round, one question on finding the kth largest element in an unsorted array. Pretty standard heap or quickselect territory.

Questions Asked (1)

Given an unsorted array of integers and a number k, return the kth largest element in the array.

Algorithms & Data Structures

Author's notes

Classic problem but I still second-guessed myself on whether to go with sorting, a min-heap, or quickselect.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., duplicates, memory limits, input size) and then propose multiple solutions: sorting, min-heap of size k, and Quickselect. Discuss trade-offs in time/space complexity and pick the optimal approach based on the context, typically Quickselect for average O(n) time or heap for O(n log k) time with streaming data.

Pro tip: Mention that Quickselect can degrade to O(n^2) worst-case and suggest using randomized pivot or Median of Medians to guarantee O(n) worst-case, showing depth beyond typical answers.

1. Clarify requirements and constraints

Ask about input size, duplicates, memory limits, and whether the array can be modified. This shows you consider practical aspects before coding.

2. Outline multiple approaches

Briefly describe sorting, min-heap, and Quickselect, highlighting their time and space complexities. This demonstrates breadth of knowledge.

3. Select and justify the optimal approach

Choose the best method based on constraints (e.g., Quickselect for in-place average O(n), heap for streaming). Explain why it's optimal.

4. Detail the algorithm and handle edge cases

Walk through the chosen algorithm step-by-step, covering edge cases like k=1, k=n, duplicates, and empty array. Mention randomization for Quickselect.

5. Analyze complexity and potential optimizations

State time and space complexity, and discuss optimizations like iterative Quickselect or using a heap for large k. Mention worst-case guarantees.

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: partition around a pivot, recurse on one side, and use randomized pivot for average O(n).
Min-heap of size k: iterate through array, maintain k largest elements, return root; good for streaming data.
Handling duplicates: ensure partition handles equal elements correctly, and kth largest is distinct by position.
Edge cases: k=1 (maximum), k=n (minimum), empty array, k out of bounds.
Trade-offs: Quickselect modifies array and has worst-case O(n^2), while heap uses O(k) extra space and is stable for streaming.

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