Meta·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Meta MLE interview that threw a scaled-up version of a classic LC problem at me. The naive backtracking approach I had in my back pocket would have worked at toy scale but completely fell apart here, and I had to think through bitmap preprocessing and DP on the fly.

Questions Asked (1)

Given a list of roughly 10,000 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.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I knew LC 1239 and started sketching backtracking before they mentioned the input size.

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AI HintsAI Generated

Suggested Approach

Model each word as a 26-bit mask of its distinct letters, then the problem reduces to selecting a subset of masks with pairwise disjoint bits that maximizes the total number of set bits. Use dynamic programming over bitmasks (or branch-and-bound with pruning) to find the optimal subset, and discuss trade-offs between exact and approximate solutions for 10,000 words.

Pro tip: Mention that you can preprocess by removing words that are subsets of others or have duplicate letters, and that the DP state can be compressed to only reachable masks, often making the solution feasible for 10,000 words. Also, note that if the alphabet were larger, you'd need a different approach, showing awareness of problem constraints.

1. Clarify and formalize

Confirm the alphabet size (assume 26 lowercase letters), that each word can be used at most once, and that the goal is to maximize distinct letters covered with no letter repeated across chosen words.

2. Preprocess words into bitmasks

For each word, compute a 26-bit integer where bit i is set if the i-th letter appears. Discard words with duplicate letters (mask popcount != word length) and remove words whose mask is a subset of another word's mask.

3. Define DP state and transition

Let dp[mask] = maximum number of distinct letters achievable using a subset of words whose combined mask is exactly 'mask'. Initialize dp[0]=0. For each word mask w, update dp[mask | w] = max(dp[mask | w], dp[mask] + popcount(w)) if mask & w == 0.

4. Optimize and analyze complexity

The naive DP over all 2^26 masks is too large, but only reachable masks from disjoint unions of word masks are considered. Use a hash map or array for reachable states, and process words in any order. Complexity is O(N * R) where R is number of reachable masks, typically much smaller than 2^26.

5. Discuss trade-offs and alternatives

If exact solution is too slow, consider greedy or beam search for approximate results. Mention that the problem is NP-hard in general (set packing), but with 26 letters and 10,000 words, the DP is practical. Also note that if words can be reused, it's a different problem.

Key Points to Mention

Bitmask representation of words for efficient set operations.
Dynamic programming over subsets (mask DP) with state as union of letters.
Preprocessing: remove duplicate-letter words and dominated masks.
Complexity analysis: O(N * 2^26) worst-case but reachable states are limited.
NP-hardness of the general set packing problem and why this instance is tractable.
Trade-offs between exact DP and heuristic approaches for large-scale data.

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