Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Meta SWE coding round, one question the whole time. Pretty standard algorithmic problem but they pushed on complexity and edge cases more than I expected.

Questions Asked (1)

Given an unsorted array of integers and a value k, find the k-th largest element. Duplicates count as separate positions. Describe your approach and analyze time and space complexity, aiming for something better than a full sort.

Algorithms & Data Structures

Author's notes

I jumped straight to the min-heap approach, size k, O(n log k) time.

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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 propose an efficient solution using Quickselect (average O(n) time, O(1) space) or a min-heap of size k (O(n log k) time, O(k) space). Explain the algorithm step-by-step, analyze time and space complexity for best, average, and worst cases, and discuss trade-offs between approaches.

Pro tip: Mention that Quickselect has O(n^2) worst-case time, but this can be mitigated with randomized pivot selection or the Median of Medians algorithm to guarantee O(n) worst-case. Also, note that for small k, a min-heap might be more practical due to lower constant factors and simplicity.

1. Clarify requirements and constraints

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

2. Propose an efficient algorithm

Describe Quickselect (partition-based) or a min-heap of size k. Explain how it finds the k-th largest without fully sorting.

3. Walk through the algorithm

Detail the steps: for Quickselect, choose a pivot, partition, and recurse on the appropriate side; for heap, build a min-heap of first k elements, then iterate through the rest, replacing the root if larger.

4. Analyze complexity

State time and space complexity for each approach, including best, average, and worst cases. Compare with sorting (O(n log n)).

5. Discuss trade-offs and edge cases

Mention when to prefer one approach over the other, and handle edge cases like k > n, duplicates, and negative numbers.

Key Points to Mention

Quickselect algorithm: partition-based, average O(n) time, O(1) space, worst-case O(n^2) unless using Median of Medians.
Min-heap approach: O(n log k) time, O(k) space, efficient for small k or streaming data.
Time and space complexity analysis for both approaches, including best, average, and worst cases.
Trade-offs: Quickselect is faster on average but has worst-case risk; heap is more predictable and works well for large n with small k.
Edge cases: k = 1 (maximum), k = n (minimum), duplicates, and empty array.
Optimization: randomized pivot selection or Median of Medians for guaranteed O(n) worst-case in Quickselect.

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