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Meta·Machine Learning Engineer·Technical Phone Screen·Senior

Senior
Jul 2026

Summary

Meta MLE interview with a debugging question around card drawing logic. Pretty focused on correctness and edge cases rather than anything flashy.

Questions Asked (1)

Q1

You're given a method that draws three cards from a list representing a table. The method has bugs. Find and fix them so that each card drawn is guaranteed to exist on the table at the moment it's drawn.

Algorithms & Data StructuresRoot Cause Analysis
Author's notes

Spent the first few minutes just reading the code slowly because I was sure I was missing something.

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AI HintsAI Generated

Suggested Approach

First, clarify the method's contract and the table's mutability semantics, then trace the code to identify bugs that violate the guarantee. Fix by ensuring each draw checks existence and handles removal atomically, and validate with edge cases like empty table or duplicates.

Pro tip: Demonstrate maturity by discussing how you'd prevent similar bugs in production, such as using immutable snapshots or thread-safe data structures, and emphasize the importance of clear invariants in ML data pipelines.

1. Clarify requirements and assumptions

Ask whether the table can change during the draws, if duplicates are allowed, and what 'guaranteed to exist' means (e.g., at the moment of draw). Confirm the expected behavior when fewer than three cards exist.

2. Trace the code and identify bugs

Walk through the method line by line, simulating draws. Look for off-by-one errors, missing bounds checks, incorrect removal (e.g., removing by value vs. index), and race conditions if concurrent.

3. Propose fixes with invariants

For each bug, suggest a fix that enforces the invariant: before each draw, verify the table is non-empty; after drawing, remove the card correctly. Consider using a while loop to retry if needed, or a data structure that supports safe removal.

4. Test with edge cases

Mention testing with an empty table, a table with exactly three cards, duplicates, and concurrent modifications. Verify that each draw returns a card that was present at that instant.

5. Discuss broader implications

Relate to ML pipelines: data availability, consistency, and fault tolerance. Suggest defensive programming and logging to catch such bugs early.

Key Points to Mention

  • Mutability and concurrency: whether the table can be modified by other threads or processes during the draws.
  • Bounds checking: ensuring the index used for drawing is within the current size of the table.
  • Correct removal: removing the drawn card by index (not by value) to handle duplicates and avoid shifting issues.
  • Atomicity: if concurrent, use locks or thread-safe collections to make check-and-remove atomic.
  • Invariant enforcement: explicitly state the invariant that each drawn card must exist at the moment of draw and show how the fix maintains it.
  • Edge cases: empty table, fewer than three cards, duplicates, and concurrent modifications.

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