Meta·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026

Summary

Coding screen for an ML Engineer role at Meta. One algorithmic question, substring matching, but they pushed past the naive solution pretty fast so be ready to actually discuss complexity and tradeoffs.

Questions Asked (1)

Given a list of strings, write a function that returns any string in the list which contains at least one other string from the same list as a contiguous substring. Return an empty string if none exist. Then walk through the time and space complexity, and describe two or three approaches that beat brute-force pairwise comparison.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The basic O(n^2 * m) nested loop solution took maybe five minutes to write but they immediately asked me to do better.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then propose a brute-force solution and analyze its complexity. Next, describe at least two optimized approaches (e.g., sorting by length and using a trie, or sorting lexicographically and checking adjacent strings) with their time/space trade-offs. Finally, discuss how to handle duplicates and return any valid string.

Pro tip: Mention that sorting by length and using a trie is a common efficient approach, but also note that sorting lexicographically and checking adjacent strings works because if a string contains another, they will be adjacent in sorted order. This shows depth and awareness of trade-offs.

1. Clarify and Restate

Confirm the problem: return any string that contains another string from the list as a contiguous substring, or empty string if none. Discuss edge cases like empty list, duplicates, and strings of same length.

2. Brute-Force Baseline

Propose checking all pairs: for each string, check if any other string is a substring. Analyze time complexity O(n^2 * L^2) or O(n^2 * L) with efficient substring search, and space O(1) extra.

3. Optimized Approach 1: Sort by Length + Trie

Sort strings by length ascending. Insert all strings into a trie. For each string, check if it contains any shorter string by traversing the trie. Time O(n * L^2) or O(n * L) with Aho-Corasick, space O(n * L).

4. Optimized Approach 2: Sort Lexicographically + Adjacent Check

Sort strings lexicographically. If a string contains another, they must be adjacent in sorted order. Check each adjacent pair for substring. Time O(n log n * L + n * L^2) or O(n log n * L) with efficient substring search, space O(1) extra.

5. Compare and Conclude

Compare approaches: trie is better for many strings with shared prefixes; sorting lexicographically is simpler and often faster in practice. Mention that the choice depends on constraints and that returning any valid string is acceptable.

Key Points to Mention

Time and space complexity of brute-force: O(n^2 * L^2) time, O(1) space.
Sorting by length ensures shorter strings are checked first, but a trie can efficiently check if a string contains any shorter string.
Lexicographic sorting property: if string A contains string B, then B will be adjacent to A in sorted order (or A will be adjacent to B).
Using a trie or Aho-Corasick automaton for efficient substring matching.
Handling duplicates: if duplicates exist, one duplicate contains the other, so return that string.
Trade-offs: trie uses more space but can be faster for large n; sorting lexicographically is simpler and uses less extra space.

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