Meta·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Meta ML engineer coding round, one question the whole time but it had a lot of layers. Seemed straightforward at first and then they kept pulling on threads I hadn't thought about.

Questions Asked (1)

Design a MovingAverage class with a constructor that takes a window size k and a next(val) method that returns the rolling average of the last k values seen. Aim for O(1) time per call and O(k) space. Walk through your data structure choice, how you handle precision or overflow, and how you'd test edge cases.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

I went straight to a circular buffer with a running sum and that part landed fine.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then propose a circular buffer (ring buffer) to store the last k values, maintaining a running sum for O(1) updates. Discuss precision (e.g., using double or Kahan summation) and overflow (e.g., using long for sum), and outline a testing strategy covering edge cases like k=1, large values, and window not yet full.

Pro tip: Mention that in ML pipelines, rolling averages are often used for smoothing metrics; emphasizing numerical stability and thread-safety shows production awareness. Also, proactively discuss how you'd handle k=0 or negative input, as interviewers at Meta value defensive coding.

1. Clarify requirements and constraints

Ask about input types (int, float), expected precision, and whether k can be zero or negative. Confirm that next(val) is called sequentially and that we need O(1) time per call.

2. Choose data structure and algorithm

Propose a circular buffer of size k to store the last k values, along with a running sum. Explain that this gives O(1) time per next() and O(k) space.

3. Address precision and overflow

Discuss using double for the average and long for the sum to avoid overflow. Mention Kahan summation or compensated summation if high precision is needed.

4. Handle edge cases and initialization

Explain behavior when fewer than k values have been seen (e.g., average of all seen so far). Handle k=0 or negative by throwing an exception or returning 0.

5. Outline testing strategy

List test cases: k=1, k>1, window not full, window full, large values, negative values, zero values, and precision checks. Suggest unit tests and property-based testing.

Key Points to Mention

Circular buffer (ring buffer) for O(1) updates and O(k) space
Running sum to avoid recomputing average each time
Use of double for average and long for sum to mitigate overflow
Kahan summation or other techniques for numerical stability
Edge cases: k=0, negative k, window not full, large/negative values
Thread-safety considerations if used in concurrent ML pipelines

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