← Meta Interview Insights

Meta·Software Engineer·Technical Phone Screen·Senior

Senior
Apr 2026

Summary

Meta interview for a Supply Chain Capacity Engineer role, focused on a string algorithm problem with a side conversation about complexity tradeoffs. Pretty straightforward coding screen but the follow-up discussion on approaches caught me a bit flat-footed.

Questions Asked (1)

Q1

Given a string, find the shortest substring that appears exactly once in it. If there are ties in length, return any one (or the lexicographically smallest, depending on what the interviewer wants).

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I started with the brute force: enumerate all substrings and count occurrences, which is roughly cubic time.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Clarify the tie-breaking rule and constraints first, then propose a suffix automaton or suffix array solution that finds the shortest unique substring in O(n) or O(n log n) time. Walk through the algorithm, analyze complexity, and discuss trade-offs versus simpler but slower approaches.

Pro tip: Mention that the shortest unique substring can be found by computing for each position the longest repeated prefix using suffix structures, then the answer is the minimum over positions of that length plus one. This shows deep insight and often impresses interviewers.

1. Clarify requirements and constraints

Ask about input size, character set, tie-breaking (any vs lexicographically smallest), and whether the substring must be contiguous. Confirm expected time/space complexity.

2. Discuss naive and intermediate approaches

Start with brute-force O(n^3) or O(n^2) using hash maps, then improve using suffix arrays or suffix automaton to achieve O(n log n) or O(n). Explain why naive is insufficient for large n.

3. Present the optimal algorithm

Describe building a suffix automaton or suffix array with LCP array. For each suffix, compute the longest prefix that appears at least twice; the shortest unique substring starting at that position has length LCP+1. Track the minimum over all positions.

4. Handle tie-breaking and edge cases

If lexicographically smallest is required, compare candidates of the same minimal length. Handle empty string, all characters repeated, and single-character unique cases.

5. Analyze complexity and trade-offs

State time and space complexity of the chosen approach. Compare with alternatives (e.g., suffix tree, rolling hash) and justify why the chosen method is optimal for the given constraints.

Key Points to Mention

  • Suffix automaton or suffix array with LCP array for efficient unique substring detection
  • The relationship: shortest unique substring at position i = LCP of suffix i with any other suffix + 1
  • Time complexity: O(n) for suffix automaton, O(n log n) for suffix array; space O(n)
  • Tie-breaking: if multiple substrings of same minimal length, either return any or find lexicographically smallest by comparing candidates
  • Edge cases: empty string, all characters identical, string length 1
  • Trade-offs: simpler O(n^2) hash map approach may be acceptable for small n but not for large inputs

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