Meta·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Meta MLE technical screen, one coding problem the whole time. Pretty focused on getting the algorithm right and then picking apart edge cases until I wanted to cry.

Questions Asked (1)

Given an array of positive integers and a target count k, you can cut each element into pieces of any positive integer length L. Find the maximum L such that the total number of pieces is at least k. Return 0 if it's not possible. Your solution should run in O(n log max(lengths)) time, and you need to handle integer overflow and edge cases explicitly.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Binary search on L was the obvious move but I fumbled the predicate setup for longer than I'd like to admit.

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AI HintsAI Generated

Suggested Approach

Recognize this as a binary search on the answer: the maximum piece length L is monotonic—if a length works, any smaller length also works. For a given L, compute the total pieces by summing floor(length_i / L) for each element, and check if it's at least k. Binary search L between 1 and max(lengths), returning 0 if even L=1 cannot produce k pieces.

Pro tip: Explicitly call out the monotonicity property and how it enables binary search, then mention that using 64-bit integers (e.g., Python ints or long long in C++) prevents overflow when summing pieces, which can exceed 32-bit limits for large arrays.

1. Clarify the problem and constraints

Restate the problem to ensure understanding: given an array of positive integers and target k, find the largest L such that cutting each element into pieces of length L yields at least k total pieces. Confirm edge cases like k=0, empty array, or k larger than sum of elements.

2. Identify monotonicity and choose binary search

Explain that the feasibility of L is monotonic: if L works, any smaller L also works. This allows binary search over the range [1, max(array)] to find the maximum feasible L.

3. Design the feasibility check

For a candidate L, compute total pieces as sum of floor(length_i / L). Use 64-bit integers to avoid overflow. If total >= k, L is feasible; otherwise not.

4. Handle edge cases and overflow

If k is 0, return max(array) (or handle as per problem statement). If even L=1 yields fewer than k pieces, return 0. Ensure the sum uses a type that can hold up to n * max(length) (e.g., long long).

5. Analyze complexity and conclude

State that binary search takes O(log max(lengths)) iterations, each doing O(n) work, for O(n log max(lengths)) time and O(1) extra space. Return the maximum feasible L.

Key Points to Mention

Monotonicity of the feasibility condition enables binary search.
Feasibility check: sum of floor(length_i / L) >= k.
Time complexity: O(n log max(lengths)) due to binary search over L.
Integer overflow: use 64-bit integers for the sum of pieces.
Edge cases: k=0, empty array, k > sum of elements, L=1 infeasible.
Return 0 if no positive L works.

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