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TikTok·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate
May 2026

Summary

TikTok software engineer coding round with two problems, one pretty mechanical and one that requires you to actually think about data structures. Nothing too wild but the second problem has some gotchas if you're not careful.

Questions Asked (2)

Q1

Given two integer arrays A and B and a threshold T, for each element in A count how many elements in B satisfy a * b >= T. Return the counts as an array.

Algorithms & Data Structures
Author's notes

Sort B and binary search for the cutoff point.

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AI HintsAI Generated

Suggested Approach

Sort array B and for each element a in A, use binary search to count how many b satisfy a*b >= T, handling positive, negative, and zero cases separately. Alternatively, sort both arrays and use two pointers to achieve O(n log n + m log m) time.

Pro tip: Clarify constraints and edge cases (e.g., zeros, negative numbers, integer overflow) before coding; this shows thoroughness and prevents subtle bugs.

1. Clarify requirements and edge cases

Ask about array sizes, value ranges, duplicates, and whether T can be negative. Discuss handling of zeros and negative numbers.

2. Choose an efficient algorithm

Propose sorting B and using binary search for each element in A, or sorting both and using two pointers. Explain time complexity trade-offs.

3. Handle sign cases explicitly

Separate B into positive, negative, and zero values. For each a, determine the threshold for b based on the sign of a and T.

4. Implement counting logic

For each a, compute the required bound for b and use binary search (or two pointers) to count qualifying elements in the sorted B.

5. Test with examples and edge cases

Walk through examples including zeros, negatives, and large values to verify correctness and discuss potential overflow.

Key Points to Mention

  • Sorting B to enable binary search or two-pointer technique
  • Handling positive, negative, and zero values of a and b separately
  • Using binary search (lower_bound/upper_bound) for counting
  • Time complexity: O((n+m) log m) with binary search or O(n log n + m log m) with two pointers
  • Space complexity: O(1) extra if sorting in place, or O(m) if copying B
  • Edge cases: empty arrays, T=0, integer overflow when multiplying large values

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

Q2

You have a list of user accounts where each account has a name and a set of identifiers. Accounts sharing at least one identifier belong to the same real user, including transitively. Merge them and return each merged group with its name and a sorted, deduplicated list of identifiers.

Algorithms & Data StructuresData Modeling
Author's notes

Classic union-find setup but I fumbled explaining it at first.

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AI HintsAI Generated

Suggested Approach

Model the problem as finding connected components in a graph where accounts are nodes and shared identifiers create edges. Use a union-find (disjoint set) data structure to efficiently merge accounts that share any identifier, then aggregate names and identifiers per component, ensuring identifiers are deduplicated and sorted.

Pro tip: Mention that union-find with path compression and union by rank gives near-constant time per operation, making it ideal for large-scale data like TikTok's; also note that handling transitive merges correctly requires processing all identifiers before finalizing groups.

1. Clarify requirements and edge cases

Confirm input format, whether names can differ within a group (and which to choose), and how to handle empty identifiers or duplicate accounts. Discuss output ordering expectations.

2. Choose union-find for merging

Explain that union-find efficiently handles transitive connections by unioning accounts that share an identifier. Use a hash map from identifier to account index to quickly find and union accounts.

3. Process all accounts and identifiers

Iterate through each account, and for each identifier, union the current account with any previously seen account having that identifier. This builds all connected components.

4. Aggregate results per component

After unions, group accounts by their root. For each group, collect all identifiers into a set, then produce a sorted list. Choose a representative name (e.g., first encountered or lexicographically smallest) and document the choice.

5. Analyze complexity and optimize

State time complexity: O(N α(N) + K log K) where N is number of accounts/identifiers and K is total identifiers for sorting. Space O(N). Mention that union-find with path compression is near-linear.

Key Points to Mention

  • Union-Find (Disjoint Set Union) with path compression and union by rank for efficient merging
  • Using a hash map to map each identifier to an account index for quick lookups
  • Handling transitive merges by processing all identifiers before finalizing groups
  • Deduplicating identifiers using a set and sorting the final list
  • Choosing a representative name per group and justifying the choice (e.g., first encountered)
  • Time and space complexity analysis, highlighting near-linear performance for large datasets

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