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InterSystems·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Interviewed for a software engineering role at Intersystems and got a classic palindrome substring problem. Pretty standard algorithmic interview, nothing too surprising, though they wanted verbal explanation of the approach rather than actual code which threw me off a little.

Questions Asked (1)

Q1

Given a string, find the longest contiguous substring that reads the same forwards and backwards. Explain your approach, time and space complexity, and any edge cases you'd handle.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

They told me upfront no coding needed, just talk through it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then present the expand-around-center approach as an optimal O(n^2) time and O(1) space solution. Explain the algorithm step-by-step, analyze complexity, and discuss trade-offs with other methods like dynamic programming or Manacher's algorithm.

Pro tip: Mention that while Manacher's algorithm achieves O(n) time, the expand-around-center method is often preferred in practice for its simplicity and low constant factors, and it's easier to implement correctly under pressure.

1. Clarify requirements and edge cases

Ask if the substring must be contiguous (yes, by definition) and confirm handling of empty strings, single characters, and case sensitivity. Discuss whether to return the substring itself or its length.

2. Propose an approach

Choose the expand-around-center method: for each character (and between characters), expand outward while characters match, tracking the longest palindrome. This handles both odd and even length palindromes.

3. Walk through the algorithm

Explain the two-pointer expansion: initialize left and right pointers at the center, expand while within bounds and characters equal, and update the longest palindrome found. Repeat for all 2n-1 centers.

4. Analyze complexity and trade-offs

State time complexity O(n^2) due to expanding from each center, and space complexity O(1) as only pointers are used. Compare with dynamic programming (O(n^2) time, O(n^2) space) and Manacher's algorithm (O(n) time, O(n) space).

5. Handle edge cases and test

Mention edge cases: empty string returns empty, single character returns itself, all same characters, and no palindrome longer than 1. Suggest testing with examples like 'babad' (returns 'bab' or 'aba') and 'cbbd' (returns 'bb').

Key Points to Mention

  • Expand around center handles both odd and even length palindromes by considering centers at characters and between characters.
  • Time complexity is O(n^2) because there are 2n-1 centers and each expansion takes O(n) in the worst case.
  • Space complexity is O(1) as we only use pointers and variables to track the longest palindrome.
  • Dynamic programming uses O(n^2) space and is less efficient in practice, though it's a valid alternative.
  • Manacher's algorithm achieves O(n) time but is complex and rarely expected in interviews unless specifically asked.
  • Edge cases include empty string, single character, all identical characters, and strings with no palindrome longer than 1.

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