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Meta·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Jun 2026

Summary

Meta ML engineer interview with a coding problem that was more algorithmic than I expected. No ML theory, just pure backtracking. Felt like a software engineering round with an ML job title on it.

Questions Asked (1)

Q1

Given a table of cards with numeric values, implement a backtracking algorithm to find the combination of 3-card sets (each set summing to 15) that maximizes the total number of cards used.

Algorithms & Data Structures
Author's notes

Took me a minute to even understand what they were optimizing for.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints (e.g., can cards be reused? are sets disjoint?) and then outline a backtracking algorithm that explores all valid 3-card sets summing to 15, using recursion with pruning to maximize the number of cards used. Discuss time/space complexity and potential optimizations like memoization or sorting to improve efficiency.

Pro tip: Emphasize that backtracking is a natural fit for this combinatorial optimization problem, but also mention that for large inputs, you might need to consider dynamic programming or branch-and-bound to handle the exponential search space. Showing awareness of trade-offs between exactness and efficiency demonstrates maturity.

1. Clarify the problem

Ask clarifying questions: Can cards be used in multiple sets? Are sets required to be disjoint? What is the input size? This ensures you understand the exact requirements before designing the algorithm.

2. Define the backtracking state

Decide on the state representation: which cards are used, current set being built, and the total cards used so far. The goal is to maximize the count of used cards.

3. Design recursive exploration

At each step, try to form a valid 3-card set summing to 15 from the remaining cards. Recurse with the updated set of used cards, and backtrack by unmarking cards to explore other combinations.

4. Implement pruning and optimization

Prune branches that cannot possibly beat the current best (e.g., if remaining cards plus used cards <= best). Sort cards or use frequency counts to avoid duplicate sets and speed up the search.

5. Analyze complexity and test

Discuss worst-case time complexity (exponential) and space complexity (recursion depth). Walk through a small example to verify correctness and consider edge cases like no valid sets.

Key Points to Mention

  • Backtracking template: choose, explore, unchoose
  • Pruning strategies to reduce search space
  • Handling duplicate cards and avoiding duplicate sets
  • Time and space complexity analysis
  • Comparison with alternative approaches like dynamic programming or integer linear programming
  • Edge cases: no valid sets, multiple optimal solutions, large input size

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