TikTok·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

TikTok SWE coding round, one question the whole time. Classic LIS problem but they pushed hard on the optimal solution and wouldn't let you sit on the O(n^2) answer.

Questions Asked (1)

Given an integer array, find the length of the longest strictly increasing subsequence.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Started with the DP approach where dp[i] is the LIS ending at index i, which is fine, but they immediately asked if I could do better.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then present the O(n^2) dynamic programming solution as a baseline, followed by the optimal O(n log n) patience sorting approach. Explain the trade-offs between time and space complexity, and discuss edge cases and potential optimizations.

Pro tip: Demonstrate strong communication by walking through a small example step-by-step, and mention that the O(n log n) solution is based on patience sorting, which shows depth of knowledge beyond rote memorization.

1. Clarify the problem

Ask clarifying questions about input size, whether the subsequence needs to be contiguous, and if there are any constraints on time/space complexity.

2. Discuss brute force and DP approach

Explain the O(n^2) dynamic programming solution where dp[i] stores the length of the longest increasing subsequence ending at index i, and how to compute it.

3. Introduce optimal approach

Describe the O(n log n) patience sorting algorithm using binary search on tails array, and why it works.

4. Analyze trade-offs

Compare the two approaches in terms of time and space complexity, and discuss when each might be preferable.

5. Handle edge cases and test

Mention edge cases like empty array, all decreasing, all equal, and walk through a small example to verify correctness.

Key Points to Mention

Time and space complexity of both O(n^2) DP and O(n log n) patience sorting approaches
Definition of strictly increasing subsequence (non-contiguous, strictly increasing values)
Use of binary search (lower_bound) in the optimal solution
Trade-offs: O(n^2) is simpler to implement but slower; O(n log n) is more efficient but requires careful implementation
Edge cases: empty array, single element, all elements equal, strictly decreasing array
Potential follow-up: reconstruct the actual subsequence, not just length

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