Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Meta coding screen, pretty straightforward. They asked me to sort an array and explain the time complexity, which felt almost too simple but I still managed to fumble the Big O explanation a bit.

Questions Asked (1)

Sort an integer array and analyze the time and space complexity of your solution.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Wrote the sort fine but then kind of stumbled when explaining the Big O.

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AI HintsAI Generated

Suggested Approach

Start by clarifying constraints (e.g., input size, data characteristics, memory limits) and then propose a sorting algorithm that balances time and space complexity, such as quicksort or mergesort. Explain the algorithm's steps, analyze its time and space complexity in best/average/worst cases, and discuss trade-offs (e.g., stability, in-place vs. extra space).

Pro tip: Meta values practical engineering: mention that in production you'd likely use the language's built-in sort (e.g., Timsort in Python/Java) which is optimized, but for interviews, implement a classic algorithm to demonstrate understanding. Also, discuss how you'd handle edge cases like already sorted arrays or duplicate values.

1. Clarify requirements and constraints

Ask about input size, data distribution, memory limits, and whether stability is required. This shows you consider real-world factors before choosing an algorithm.

2. Choose an algorithm and justify

Select a sorting algorithm (e.g., quicksort, mergesort, heapsort) based on constraints. Briefly explain why it fits (e.g., quicksort for in-place average O(n log n), mergesort for stability).

3. Explain the algorithm step-by-step

Walk through the algorithm's logic clearly, using a small example if helpful. Focus on key operations like partitioning or merging.

4. Analyze time and space complexity

Provide best, average, and worst-case time complexity, and space complexity (including recursion stack). Mention if it's in-place or uses extra space.

5. Discuss trade-offs and alternatives

Compare with other algorithms (e.g., quicksort vs. mergesort) and mention practical considerations like cache performance, stability, and built-in sort functions.

Key Points to Mention

Time complexity: O(n log n) average for quicksort/mergesort, O(n^2) worst-case for quicksort (with poor pivot), O(n log n) guaranteed for mergesort/heapsort.
Space complexity: O(log n) for quicksort (recursion stack), O(n) for mergesort (extra array), O(1) for heapsort (in-place).
Stability: mergesort is stable, quicksort and heapsort are not; important if sorting objects by multiple keys.
In-place vs. out-of-place: quicksort and heapsort are in-place, mergesort requires extra space.
Practical considerations: built-in sorts (e.g., Timsort) are often preferred in production; consider data characteristics (e.g., nearly sorted data favors insertion sort).
Edge cases: empty array, single element, duplicates, already sorted, reverse sorted.

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