Meta·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026

Summary

Meta ML engineer screen, one coding problem, felt straightforward on the surface but the expected solution was binary search and I definitely went the slow route first.

Questions Asked (1)

Given a sorted array of integers, return the maximum of the count of positive numbers and the count of negative numbers. Zero counts as neither.

Algorithms & Data Structures

Author's notes

My first instinct was just to iterate through the whole thing and tally up.

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AI HintsAI Generated

Suggested Approach

Since the array is sorted, use binary search to find the first non-negative element (or the boundary between negatives and non-negatives). Then compute the count of negatives as the index of that boundary, and the count of positives as the total length minus the index of the first positive element (skipping zeros). Return the maximum of these two counts.

Pro tip: Clarify upfront whether the array can contain zeros and how they should be treated, and mention that binary search gives O(log n) time, which is optimal for a sorted array. Also, handle edge cases like all negatives, all positives, or all zeros.

1. Clarify assumptions and edge cases

Confirm with the interviewer that the array is sorted in non-decreasing order, may contain zeros, and that zeros are excluded from both counts. Discuss edge cases such as empty array, all negatives, all positives, or all zeros.

2. Find the boundary between negatives and non-negatives

Use binary search to find the index of the first element that is >= 0. This index equals the number of negative numbers.

3. Find the boundary between non-positives and positives

Use binary search to find the index of the first element that is > 0. The number of positive numbers is the total length minus this index.

4. Compute and return the maximum count

Compare the count of negatives and the count of positives, and return the larger value. If the array is empty, return 0.

5. Analyze complexity and test

State that the time complexity is O(log n) due to binary search, and space complexity is O(1). Walk through a few test cases to verify correctness.

Key Points to Mention

Leveraging the sorted property to achieve O(log n) time with binary search.
Handling zeros correctly: they are neither positive nor negative, so they must be excluded from both counts.
Edge cases: empty array, all negative, all positive, all zeros, and arrays with zeros in between.
Using two binary searches: one for the first non-negative and one for the first positive.
Time and space complexity analysis: O(log n) time, O(1) space.
Potential follow-up: if the array is not sorted, a linear scan would be O(n), but sorting would take O(n log n).

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