Meta·Machine Learning Engineer·Technical Phone Screen·Senior

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May 2026Remote

Summary

Meta MLE phone screen, one coding problem the whole time. Pretty focused session, no fluff, just the algorithm.

Questions Asked (1)

Given a small list of words, find a subset where no letter appears more than once across all chosen words, and the total number of distinct letters covered is maximized. Implement a backtracking solution.

Algorithms & Data Structures

Author's notes

I knew the LC problem this was based on so I wasn't totally lost, but I fumbled the pruning logic at first.

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AI HintsAI Generated

Suggested Approach

Model each word as a bitmask of its letters, then use backtracking to explore subsets, pruning branches where a letter repeats. Track the maximum distinct letter count and the corresponding subset, and analyze time/space complexity.

Pro tip: Precompute bitmasks and skip words that have duplicate letters internally, as they can never be part of a valid subset. Also, sort words by number of distinct letters descending to potentially find good solutions early and prune more aggressively.

1. Clarify and Model

Confirm that words are case-insensitive and letters are a-z. Represent each word as a 26-bit integer where bit i is set if the i-th letter appears. Filter out words with internal duplicate letters.

2. Backtracking Design

Recursively consider each word: either include it (if its mask doesn't intersect the current mask) or skip it. Maintain the current mask and the count of distinct letters (popcount).

3. Pruning and Optimization

Prune when the current count plus the maximum possible additional letters from remaining words cannot exceed the best found. Sort words by popcount descending to improve pruning.

4. Track and Return

Keep track of the best mask and its popcount. After exploring all subsets, return the subset of words corresponding to the best mask.

5. Complexity Analysis

Analyze time complexity: worst-case O(2^n) but pruning reduces practical runtime. Space complexity O(n) for recursion stack. Discuss trade-offs with alternative approaches like DP over masks.

Key Points to Mention

Bitmask representation of words for efficient intersection checks.
Backtracking with pruning to avoid exploring invalid subsets.
Preprocessing: remove words with duplicate letters and sort by popcount.
Time complexity: exponential in worst case but often much faster due to pruning.
Space complexity: O(n) for recursion stack, O(1) for masks.
Potential optimization: use DP over letter masks if the alphabet is small.

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