TikTok·Machine Learning Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

TikTok ML Engineer interview with a coding round that leaned heavily on array problems. The subarray sum question came with follow-up variants that tested whether you actually understood the underlying mechanics or just memorized a solution.

Questions Asked (1)

Given an integer array and a target value k, count the total number of contiguous subarrays whose elements sum to k. Follow-ups: (a) simplify to returning a boolean for whether any such subarray exists, and (b) if all values are positive, can you solve it with O(1) extra space using a two-pointer approach?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Knew the prefix sum trick going in so the base problem was fine.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., negative numbers, array size) and then present the optimal O(n) solution using a hash map to store prefix sums and their frequencies. For the follow-ups, explain the boolean variant as a simplification of the same approach, and for positive numbers, describe the two-pointer sliding window technique with O(1) space. Emphasize trade-offs between time and space complexity and discuss edge cases.

Pro tip: Mention that the prefix sum approach can be adapted to handle streaming data or large datasets by processing elements one by one, which is relevant for ML pipelines at TikTok. Also, explicitly state that the two-pointer approach fails with negative numbers, showing you understand the underlying assumptions.

1. Clarify the problem

Ask about constraints: array size, possible values (negative, zero, positive), and whether the array is static. Confirm the definition of 'contiguous subarray' and that k can be any integer.

2. Present the optimal solution

Describe the O(n) time, O(n) space approach using a hash map to store prefix sums and their frequencies. Walk through the algorithm: initialize map with {0:1}, iterate through array, update cumulative sum, check if (sum - k) exists in map, and add its frequency to count.

3. Address follow-up (a): boolean variant

Explain that the same prefix sum approach can be used, but instead of counting, return true as soon as a subarray sum equals k is found. This can potentially early-exit, but worst-case time remains O(n).

4. Address follow-up (b): positive numbers only

Describe the two-pointer sliding window technique: maintain a window [left, right] and current sum. Expand right to add elements, and while sum > k, shrink from left. If sum == k, return true (or count). This uses O(1) extra space and O(n) time.

5. Discuss trade-offs and edge cases

Compare the approaches: hash map works for any integers but uses O(n) space; two-pointer only works for non-negative numbers but is space-efficient. Mention edge cases: empty array, k=0, all zeros, large arrays, and integer overflow.

Key Points to Mention

Prefix sum technique and its use in subarray sum problems
Hash map to store prefix sum frequencies for O(1) lookups
Time and space complexity: O(n) time, O(n) space for general case
Two-pointer/sliding window approach for positive numbers with O(1) space
Handling negative numbers: why two-pointer fails and hash map is needed
Edge cases: empty array, k=0, all zeros, large sums (overflow)

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