Meta·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Meta MLE technical screen focused on a coding problem in a friend recommendation system. Pretty standard setup but the follow-up discussion on scaling was where things got interesting.

Questions Asked (1)

Given a User class with a friend list and a valid_recommend function, implement random_recommend(user) that returns a uniformly random valid candidate, or None if no valid candidate exists.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

The naive path is obvious: filter all users through valid_recommend, throw them in a list, pick randomly.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem constraints and the definition of a valid candidate. Then, design an algorithm that efficiently collects all valid candidates and selects one uniformly at random, handling edge cases like an empty candidate set. Finally, analyze time and space complexity and discuss potential optimizations for large-scale systems.

Pro tip: In ML engineering interviews, emphasize the importance of uniform sampling for unbiased training data and discuss how this function could be integrated into a recommendation pipeline. Also, mention that if the friend list is huge, a two-pass approach (count then select) avoids storing all candidates, which is crucial for scalability.

1. Clarify requirements and constraints

Ask about the size of the friend list, the expected frequency of calls, and whether the User class can be modified. Confirm that 'valid' means valid_recommend returns True and that the candidate must be a friend.

2. Design the algorithm

Propose a method to iterate through the friend list, collect all valid candidates, and then use random.choice to select one. Alternatively, use reservoir sampling to select uniformly without storing all candidates.

3. Handle edge cases

Check if the friend list is empty or if no valid candidates exist, and return None. Also consider if the user itself is in the friend list and should be excluded.

4. Analyze complexity and trade-offs

Discuss time complexity O(n) and space complexity O(k) where k is the number of valid candidates. Compare with reservoir sampling which uses O(1) space but still O(n) time.

5. Discuss scalability and integration

Mention how this function could be used in a larger recommendation system, and potential optimizations like precomputing valid candidates or using approximate methods for very large graphs.

Key Points to Mention

Uniform random selection ensures fairness and avoids bias in recommendations.
Reservoir sampling allows uniform selection in a single pass with O(1) space, useful for streaming or large data.
Time complexity is O(n) where n is the number of friends; space complexity can be O(1) with reservoir sampling or O(k) with collection.
Edge cases: empty friend list, no valid candidates, user in friend list.
Integration with ML pipelines: this function could be used for negative sampling or data augmentation.
Potential trade-offs: exact uniformity vs. efficiency; caching valid candidates if the friend list is static.

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