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Meta·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
May 2026Remote

Summary

Meta ML engineer coding round, one problem the whole session. Classic bitmask DP problem that I'd seen before but still managed to overthink in the moment.

Questions Asked (1)

Q1

Given a list of strings, find the maximum length of a concatenation of a chosen subset such that every character in the result is unique.

Algorithms & Data Structures
Author's notes

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Suggested Approach

Use bitmasking to represent the set of characters in each string, pre-filtering strings with duplicate characters. Then, use dynamic programming to find the maximum length concatenation of a subset with no overlapping characters.

Pro tip: Mention that the DP state can be optimized by iterating over masks in increasing order and updating only when the new mask is a superset, ensuring O(2^26) time but with pruning. Also, note that the problem is NP-hard in general but feasible due to the 26-character limit.

1. Preprocess strings

For each string, compute a bitmask of its characters and its length. Discard strings with duplicate characters (i.e., where the bitmask's popcount is less than the string length).

2. Initialize DP array

Create an array dp of size 2^26 (or use a hash map for sparse states) initialized to -1, with dp[0] = 0. This array will store the maximum total length for each character mask.

3. Iterate over masks

For each mask from 0 to 2^26-1, if dp[mask] is valid, consider each valid string's bitmask. If the string's mask does not overlap with mask, update dp[mask | string_mask] = max(dp[mask | string_mask], dp[mask] + length).

4. Track maximum

Keep track of the maximum value in dp during the iteration, as the answer is the maximum length over all masks.

5. Optimize if needed

If the number of valid strings is small, use a recursive backtracking approach with pruning instead of full DP to save memory and time.

Key Points to Mention

  • Bitmask representation of character sets for efficient overlap checking.
  • Filtering out strings with duplicate characters as they can never be part of a valid concatenation.
  • Dynamic programming over subsets (masks) to maximize total length.
  • Time complexity: O(2^26 * N) where N is number of valid strings, but can be optimized with sparse DP.
  • Space complexity: O(2^26) for DP array, but can be reduced using hash map for sparse states.
  • Alternative approach: backtracking with pruning for small N, but DP is more systematic.

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