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Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Meta coding screen for a software engineer role, basically one array problem with a couple follow-up constraints tacked on. Not a bad experience, just felt like the follow-ups were where they actually decided if you knew your stuff.

Questions Asked (1)

Q1

Given an integer array and an integer k, find the k-th largest element in the array.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The base case is pretty straightforward, sort and index.

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AI HintsAI Generated

Suggested Approach

Start by clarifying constraints (e.g., array size, value range, duplicates, whether k is 1-indexed). Then present multiple solutions: sorting (O(n log n)), min-heap of size k (O(n log k)), and Quickselect (average O(n)). Discuss trade-offs and choose the best based on constraints, mentioning edge cases and potential optimizations.

Pro tip: Meta values production-ready code and trade-off analysis. After presenting the optimal solution, mention how you would handle duplicates (e.g., using a hash map or three-way partitioning) and discuss the worst-case of Quickselect (O(n^2)) and how to mitigate it with random pivot selection.

1. Clarify requirements and constraints

Ask about input size, value range, duplicates, and whether k is 1-indexed. Confirm expected time/space complexity and if the array can be modified.

2. Propose multiple approaches

Outline sorting, min-heap, and Quickselect. Briefly explain each and their time/space complexities.

3. Analyze trade-offs

Compare approaches: sorting is simple but O(n log n); heap is O(n log k) and good for streaming; Quickselect is average O(n) but worst-case O(n^2). Choose based on constraints.

4. Implement the chosen solution

Write clean code for the selected approach, handling edge cases like k=1, k=n, empty array, and duplicates.

5. Test and optimize

Walk through examples, test edge cases, and discuss potential optimizations (e.g., random pivot, early termination).

Key Points to Mention

  • Time and space complexity of each approach (sorting, heap, Quickselect)
  • Handling duplicates: ensure k-th largest is correctly identified (e.g., use a set or three-way partition)
  • Quickselect algorithm details: partition, pivot selection, and average vs worst-case complexity
  • Min-heap approach: maintaining a heap of size k and its suitability for streaming data
  • Edge cases: k=1, k=n, empty array, negative numbers, and large input sizes
  • Trade-offs between simplicity (sorting) and efficiency (Quickselect) in a production environment

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