Meta·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Apr 2026Remote

Summary

Meta MLE technical screen with one coding problem. The twist was they let you use AI for implementation but still grilled you on verbal explanations and complexity analysis, which I wasn't fully prepared for.

Questions Asked (1)

Given a list of strings, determine if any string in the list is a substring of another string in the list. For example, given ["programming", "am", "pro"], return "programming" since it contains both "am" and "pro". A brute force solution is provided; write 2 to 3 more efficient approaches.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went in expecting maze or graph stuff and got string containment instead.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then discuss the brute force approach and its complexity. Propose 2-3 more efficient algorithms, such as sorting by length and using a trie, or sorting lexicographically and checking adjacent strings, and analyze their time and space complexities. Conclude by comparing trade-offs and selecting the best approach for the given constraints.

Pro tip: Mention that in practice, you can optimize by sorting strings by length and using a trie, but also note that for small lists, brute force might be acceptable. Always discuss trade-offs and ask about constraints before diving into solutions.

1. Clarify requirements and constraints

Ask about input size, character set, and whether we need to return all such strings or just one. This helps determine the appropriate algorithm.

2. Analyze brute force approach

Explain the O(n^2 * L^2) brute force method of comparing each pair of strings using substring checks, and identify its inefficiencies.

3. Propose efficient approaches

Describe 2-3 optimized algorithms: (1) Sort by length and use a trie to check for substrings, (2) Sort lexicographically and check adjacent strings, (3) Use a suffix automaton or Aho-Corasick for multiple pattern matching.

4. Analyze time and space complexity

For each approach, provide Big-O analysis and discuss trade-offs between time and space, and implementation complexity.

5. Select and justify best approach

Based on constraints, recommend the most suitable approach, e.g., trie for large lists with many strings, or sorting for simpler implementation.

Key Points to Mention

Time and space complexity of each approach
Trade-offs between preprocessing (e.g., building a trie) and query time
Handling edge cases: empty strings, duplicates, no substring found
Use of sorting by length to reduce unnecessary comparisons
Trie data structure for efficient prefix matching
Aho-Corasick algorithm for multiple pattern matching in a single pass

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