TikTok·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

TikTok software engineer coding round, one algorithmic problem the whole session. Pretty standard selection algorithm question but the complexity analysis at the end is where things got interesting.

Questions Asked (1)

Given an unsorted array of integers and a positive integer k, find the k-th largest element. Walk through your algorithm design, write working code, and analyze both average-case time and space complexity.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I jumped straight to quickselect without much preamble, which I think was the right call.

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AI HintsAI Generated

Suggested Approach

Start by clarifying constraints and edge cases, then present two approaches: sorting and Quickselect. Emphasize Quickselect's average O(n) time and O(1) space, walk through the partition logic, and provide clean code. Conclude with complexity analysis and discuss trade-offs like worst-case O(n^2) and the heap alternative.

Pro tip: Mention that you can use a min-heap of size k for O(n log k) time, which is better for streaming data or when k is small, showing you consider practical scenarios beyond the optimal average-case solution.

1. Clarify requirements and edge cases

Ask about input size, duplicates, memory constraints, and whether the array can be modified. Confirm that k is 1-indexed and within bounds.

2. Discuss possible approaches

Mention sorting (O(n log n)), min-heap of size k (O(n log k)), and Quickselect (average O(n)). Explain why Quickselect is optimal for average-case time and space.

3. Explain Quickselect algorithm

Describe partitioning around a pivot and recursively searching only the side containing the k-th largest. Use a random pivot to avoid worst-case on sorted input.

4. Write code and test

Implement the partition function and the recursive or iterative Quickselect. Walk through a small example to verify correctness, including duplicates.

5. Analyze complexity and trade-offs

State average O(n) time and O(1) space (ignoring recursion stack). Discuss worst-case O(n^2) and how randomization mitigates it. Compare with heap approach for streaming or when k is small.

Key Points to Mention

Quickselect uses partitioning similar to Quicksort but only recurses on one side, giving average O(n) time.
Random pivot selection avoids worst-case O(n^2) on already sorted or adversarial inputs.
Space complexity is O(1) for iterative implementation, or O(log n) for recursive due to call stack.
Min-heap of size k gives O(n log k) time and O(k) space, which is better when k is small or data is streaming.
Duplicates are handled naturally by the partition scheme if using three-way partitioning or careful comparison.
Edge cases: k=1 (maximum), k=n (minimum), empty array, k out of bounds.

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