Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Meta SWE coding round, one algorithmic question about finding the Kth largest element in an array. Pretty focused session, no fluff.

Questions Asked (1)

Given an array of integers and a value K, find the Kth largest element efficiently. What data structure would you use and why?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was to sort it and index from the end, which works but they pushed back on complexity pretty fast.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., array size, memory limits, whether the array can be modified) and then present multiple solutions with trade-offs. Focus on the optimal heap-based approach (O(n log k)) and the Quickselect algorithm (average O(n)), explaining when each is appropriate. Conclude by discussing data structure choices and their impact on time/space complexity.

Pro tip: At Meta, interviewers value practical trade-offs: mention that Quickselect has poor worst-case performance (O(n^2)) and that a heap guarantees O(n log k) but uses O(k) space. Also, note that for small K, a min-heap of size K is more memory-efficient than sorting the entire array.

1. Clarify requirements and constraints

Ask about input size, memory limits, whether the array can be modified, and if K is guaranteed valid. This shows you consider edge cases and scalability.

2. Discuss brute-force and sorting approaches

Mention that sorting the array takes O(n log n) time and then indexing gives the answer, but it's inefficient for large n. This sets a baseline for comparison.

3. Present heap-based solution

Explain using a min-heap of size K: iterate through the array, push elements, and if size exceeds K, pop the smallest. The root will be the Kth largest. Time: O(n log k), Space: O(k).

4. Present Quickselect solution

Describe Quickselect (based on QuickSort partition): pick a pivot, partition, and recurse on the side containing the Kth largest. Average O(n) time, worst-case O(n^2), and O(1) extra space if done in-place.

5. Compare and choose based on context

Discuss trade-offs: heap is better for streaming data or when K is small; Quickselect is faster on average for static arrays but has worst-case risk. Mention that a heap is more predictable and easier to implement.

Key Points to Mention

Time and space complexity of each approach (sorting, heap, Quickselect)
Min-heap of size K vs. max-heap of size n: why min-heap is more efficient
Quickselect's average O(n) but worst-case O(n^2) and how to mitigate (random pivot)
When to use each: e.g., heap for streaming or small K, Quickselect for static arrays
Edge cases: K=1 (max), K=n (min), duplicates, negative numbers
Meta's emphasis on scalability and practical trade-offs

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