Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jul 2026Remote

Summary

Meta SWE coding round with a friend recommendation problem. Pretty standard randomization + filtering exercise but the follow-up about reproducibility with a seed tripped me up a bit.

Questions Asked (1)

Implement a random friend recommendation function that takes a user, a list of all users, an optional count k, and an optional seed. It should return up to k users who are not already friends with the given user and not the user themselves, with no duplicate results.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The filtering part was easy enough, just exclude the user's own id and anyone already in their friend list, then sample from what's left.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then propose a solution that filters out the user and their friends, uses a seeded random shuffle for reproducibility, and returns up to k unique users. Discuss trade-offs between different approaches (e.g., full shuffle vs. reservoir sampling) and consider scalability for large user bases.

Pro tip: Mention that using a seeded random generator ensures deterministic results for testing and debugging, and consider the memory/time trade-offs when dealing with massive datasets—reservoir sampling can be more efficient than shuffling the entire list.

1. Clarify requirements and edge cases

Ask about input types, expected behavior when k exceeds available candidates, handling of duplicate users, and whether the seed should affect the order or just the selection.

2. Design the algorithm

Outline steps: filter out the user and their friends, then randomly select up to k unique users. Choose between shuffling the filtered list or using reservoir sampling for efficiency.

3. Implement with randomness and seed

Use a seeded random number generator (e.g., random.Random(seed)) to ensure reproducibility. If shuffling, shuffle the filtered list and take the first k; if using reservoir sampling, iterate through candidates and maintain a reservoir of size k.

4. Handle edge cases and validate

Ensure no duplicates, handle k=0, k larger than available candidates, and empty friend lists. Test with different seeds to verify randomness and reproducibility.

5. Discuss trade-offs and scalability

Compare time/space complexity of approaches: shuffling is O(n) time and space, while reservoir sampling is O(n) time and O(k) space. For large n, reservoir sampling is more memory-efficient.

Key Points to Mention

Filtering out the user and their friends to avoid recommending existing connections or self.
Using a seeded random generator for reproducibility and testability.
Ensuring uniqueness of recommendations (no duplicates).
Handling edge cases: k=0, k > available candidates, empty friend list.
Trade-offs between shuffling and reservoir sampling in terms of time and space complexity.
Scalability considerations for large user bases (e.g., memory usage, streaming data).

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