Meta·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Did a technical phone screen for an MLE role at Meta. One coding question with a follow-up on complexity, and I fumbled the analysis pretty badly on the follow-up.

Questions Asked (1)

Find the Kth largest element in an unsorted array. What's the most efficient approach, and can you do better than O(n log k)?

Algorithms & Data Structures

Author's notes

Got through the main part fine, just a couple of syntax hiccups nothing serious.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., array size, value range, duplicates) and then present a progression of solutions: sorting, min-heap of size k, and Quickselect. Emphasize that Quickselect achieves average O(n) time, which is better than O(n log k), and discuss its worst-case O(n^2) and how to mitigate it with random pivoting or Median of Medians.

Pro tip: Mention that for streaming data or when k is small, a heap is preferable, but for a static array, Quickselect is optimal. Also, note that Python's heapq.nlargest uses a heap and is O(n log k), but Quickselect can be implemented in-place for O(1) extra space.

1. Clarify requirements and constraints

Ask about input size, value range, duplicates, and whether the array can be modified. This shows attention to detail and helps choose the right approach.

2. Discuss naive and heap-based solutions

Mention sorting (O(n log n)) and min-heap of size k (O(n log k)) as baselines, highlighting their trade-offs in time and space.

3. Introduce Quickselect for average O(n)

Explain the partition-based selection algorithm, its average O(n) time, and how it avoids full sorting by discarding half the array each step.

4. Address worst-case and optimizations

Acknowledge Quickselect's O(n^2) worst-case and propose randomization or Median of Medians to guarantee O(n) worst-case.

5. Compare and conclude with recommendation

Summarize trade-offs and recommend Quickselect for static arrays, heap for streaming or when k is small, and mention Python's heapq.nlargest as a practical alternative.

Key Points to Mention

Time complexity: O(n log k) for heap, O(n) average for Quickselect, O(n) worst-case with Median of Medians.
Space complexity: O(k) for heap, O(1) for in-place Quickselect.
Quickselect algorithm: partition around a pivot, recurse on one side based on pivot's final position.
Randomized pivot selection to avoid worst-case on sorted or adversarial inputs.
Median of Medians for deterministic O(n) worst-case, though with higher constant factors.
Applicability: Quickselect modifies the array; if immutability is required, use a heap or copy.

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