TikTok·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

TikTok software engineer interview with two algorithmic problems. The first was a meaty array question with multiple required approaches, and the second was a string rotation variant with a twist on returning move counts. Pretty standard coding round but they pushed hard on complexity tradeoffs.

Questions Asked (2)

Given an unsorted array of integers and a number k, return the k largest elements in descending order. You need to implement at least two approaches, talk through time and space complexity for each, and explain how you'd handle duplicates and very large inputs like streaming data.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This one took longer than I expected because they actually wanted two working implementations, not just a description.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., array size, k range, duplicates, memory limits) and then present two distinct approaches: sorting and a heap-based method. For each, analyze time and space complexity, discuss how duplicates are handled, and explain adaptations for streaming data using a min-heap of size k.

Pro tip: Emphasize the trade-offs between approaches: sorting is simple but O(n log n), while the heap approach is O(n log k) and better for large n or streaming. Mention that for streaming, a min-heap of size k efficiently maintains the top k elements.

1. Clarify requirements and constraints

Ask about input size, k relative to n, duplicate handling, memory limits, and whether the input is static or streaming. This shows you consider practical constraints before coding.

2. Present approach 1: Sorting

Sort the array in descending order and take the first k elements. Time complexity O(n log n), space O(1) if in-place or O(n) if not. Handles duplicates naturally.

3. Present approach 2: Min-heap of size k

Iterate through the array, maintaining a min-heap of the k largest elements seen so far. For each element, if heap size < k, push; else if element > heap top, pop and push. Finally, extract elements and sort descending. Time O(n log k), space O(k).

4. Discuss duplicates and streaming

Explain that duplicates are handled by including them as separate elements (unless specified otherwise). For streaming data, the min-heap approach works well as it processes elements one by one and only stores k elements.

5. Compare and recommend

Summarize trade-offs: sorting is simpler but less efficient for large n; heap is better for large n or streaming. Recommend based on constraints.

Key Points to Mention

Time and space complexity of each approach: sorting O(n log n) time, O(1) or O(n) space; heap O(n log k) time, O(k) space.
Handling duplicates: include them as separate elements unless specified otherwise; heap approach naturally includes duplicates.
Streaming data: min-heap of size k is ideal as it processes elements in one pass and uses O(k) memory.
Edge cases: k=0, k>=n, empty array, negative numbers.
Alternative approaches: quickselect for average O(n) time, but worst-case O(n^2) and not suitable for streaming.
Implementation details: use a min-heap (priority queue) and reverse the result to get descending order.

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

Given two strings s and goal, you can repeatedly move the first character of s to the end. Can s be transformed into goal this way, and if so what is the minimum number of moves required? Return -1 if it's not possible.

Algorithms & Data Structures

Author's notes

Classic rotation check but the minimum moves part is what they actually care about.

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AI HintsAI Generated

Suggested Approach

First, check if s and goal are rotations of each other by verifying they have the same length and that goal is a substring of s + s. If not, return -1. If they are rotations, find the minimum number of moves by locating the first occurrence of goal in s + s, which gives the rotation offset, and return that index.

Pro tip: Mention that the minimum moves is the index of goal in s + s, but only if that index is less than the length of s; otherwise, it's not a valid rotation. Also, note that if s and goal are identical, the answer is 0.

1. Check length and character composition

If s and goal have different lengths, return -1 immediately. Also, if they have the same length but different character counts, they cannot be rotations, so return -1.

2. Check if goal is a rotation of s

Concatenate s with itself to form s+s. If goal is not a substring of s+s, then s cannot be transformed into goal, so return -1.

3. Find the minimum number of moves

Find the first occurrence of goal in s+s. The starting index of this occurrence is the minimum number of moves required. If the index is greater than or equal to the length of s, it means the rotation is not valid (but since we already checked substring, it should be less than length).

4. Handle edge cases

If s equals goal, the answer is 0. Also, consider empty strings: if both are empty, return 0; if one is empty and the other is not, return -1.

Key Points to Mention

Rotation check using s+s and substring search
Minimum moves equals the index of goal in s+s
Time complexity: O(n) using KMP or built-in substring search, O(n^2) naive
Space complexity: O(n) for s+s
Edge cases: identical strings, different lengths, different character counts
Alternative approach: simulate moves but inefficient

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