← Bytedance Interview Insights

Bytedance·Machine Learning Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Bytedance ML engineer screen, one coding question the whole time. Pretty standard dynamic programming territory but the linear time constraint is what makes it interesting. Left feeling okay about it.

Questions Asked (1)

Q1

Given an integer array that can contain positive numbers, negative numbers, and zeros, find the maximum product you can get from any non-empty contiguous subarray. Solve it in linear time.

Algorithms & Data Structures
Author's notes

The basic idea clicked fast but I kept second-guessing myself on the negative number case.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Use dynamic programming with two variables to track the maximum and minimum product ending at the current position, updating them as you iterate through the array. The minimum product is crucial because multiplying by a negative number can turn it into the maximum. Keep a global maximum to record the best product seen so far.

Pro tip: Explicitly handle zeros by resetting both max and min to 1 (or the current element) because any subarray containing a zero has product zero, which is never optimal unless all numbers are zero. Also, clarify that the subarray must be non-empty, so initialize with the first element.

1. Clarify and confirm

Restate the problem to ensure understanding: find the maximum product of any non-empty contiguous subarray in O(n) time. Ask about constraints (e.g., array size, integer range) and edge cases (all zeros, single element).

2. Define state variables

Maintain two variables: max_prod and min_prod, representing the maximum and minimum product of subarrays ending at the current index. Also keep a global max_product to track the overall maximum.

3. Initialize and iterate

Initialize max_prod, min_prod, and max_product to the first element. Iterate from the second element, and for each number, compute new max_prod and min_prod using the previous values and the current number.

4. Update and handle zeros

Update max_product with the new max_prod. If the current number is zero, reset max_prod and min_prod to 1 (or handle by setting to the next element) to effectively start a new subarray after the zero.

5. Return result and analyze

After the loop, return max_product. Discuss time complexity O(n) and space complexity O(1), and mention that this approach handles negative numbers and zeros correctly.

Key Points to Mention

  • Dynamic programming with O(n) time and O(1) space.
  • Tracking both maximum and minimum products due to sign flips with negative numbers.
  • Handling zeros by resetting the running products.
  • Edge cases: all negative numbers, all zeros, single element array.
  • Comparison with brute-force O(n^2) approach to highlight efficiency.
  • Potential follow-up: returning the subarray itself, not just the product.

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