Bytedance·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026

Summary

Bytedance ML Engineer interview with a pretty gnarly algorithm problem. One question, prefix sum style, and they made it clear that brute force wasn't going to cut it.

Questions Asked (1)

Given an integer array and two integers (modulo and target), count contiguous subarrays where the number of 'special' elements (those whose value mod modulo equals target) has a remainder of target when divided by modulo. Return the count as a 64-bit integer. An O(n^2) solution is not acceptable.

Algorithms & Data Structures

Author's notes

I stared at this for a solid minute before realizing it's a prefix sum + frequency map problem.

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AI HintsAI Generated

Suggested Approach

Transform the array into a binary sequence where 1 indicates a special element (value % modulo == target). Compute prefix sums of this binary array, then the problem reduces to counting pairs (i, j) with i < j such that (prefix[j] - prefix[i]) % modulo == target. Use a hash map to store frequencies of prefix sums modulo modulo and count valid pairs in O(n) time.

Pro tip: Clarify that the condition is on the count of special elements modulo modulo, not on the sum of elements. Also, handle negative modulo correctly by ensuring the remainder is non-negative.

1. Understand the problem

Restate the problem: count contiguous subarrays where the number of special elements (value % modulo == target) modulo modulo equals target. Confirm that O(n^2) is unacceptable and aim for O(n).

2. Transform to binary array

Create a binary array B where B[i] = 1 if arr[i] % modulo == target, else 0. This simplifies the problem to counting subarrays with sum modulo modulo equal to target.

3. Use prefix sums and hash map

Compute prefix sums of B. For each prefix sum S, we need to find previous prefix sums P such that (S - P) % modulo == target. Use a hash map to store frequencies of prefix sums modulo modulo.

4. Count valid subarrays

Initialize the hash map with {0: 1} to handle subarrays starting from index 0. Iterate through the array, update the prefix sum, compute the required previous remainder, add its frequency to the count, and then update the hash map with the current remainder.

5. Return the count

After processing all elements, return the total count as a 64-bit integer (e.g., long in Java, int64 in Python).

Key Points to Mention

Time complexity O(n) and space complexity O(modulo) due to hash map.
Handling modulo of negative numbers: ensure remainder is non-negative by using ((x % modulo) + modulo) % modulo.
Using a hash map (dictionary) to store frequencies of prefix sums modulo modulo.
Initializing the hash map with {0: 1} to account for subarrays starting at index 0.
The count can be large, so use 64-bit integer (e.g., long long in C++, long in Java).
Edge cases: modulo = 1, target = 0; all elements special; no special elements.

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