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Upstart·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Interviewed for a software engineering role at Upstart and got a string filtering problem that looked simple on the surface but had a few layers worth thinking through.

Questions Asked (1)

Q1

Given a list of strings and a reference string s, return only the strings from the list that share at least one character with s. What's an efficient way to implement this, and how do you handle case sensitivity and Unicode?

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The core solution came pretty naturally.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: case sensitivity and Unicode handling. Then propose an efficient solution using a set of characters from the reference string for O(1) lookups, and iterate through each string checking for intersection. Discuss trade-offs between preprocessing the reference string versus each string, and how to handle Unicode normalization and case folding.

Pro tip: Mention that using a set of characters from the reference string is efficient, but if the list is large and strings are short, checking each string's characters against the set might be faster than building sets for each string. Also, Unicode normalization (e.g., NFC) is crucial for correct character comparison.

1. Clarify requirements

Ask about case sensitivity (case-sensitive vs. case-insensitive) and Unicode handling (e.g., normalization, grapheme clusters). Confirm if the reference string can be empty or if strings can be empty.

2. Choose data structure

Use a set of characters from the reference string for O(1) membership checks. For case-insensitive, convert both to a common case (e.g., lower) before building the set and checking.

3. Iterate and filter

For each string in the list, check if any character is in the set. If yes, include it. This is O(n * m) where n is number of strings and m is average length, but with early exit.

4. Handle Unicode

Normalize strings to a standard form (e.g., NFC) before comparison to ensure characters like 'é' are treated consistently. Consider using code points or grapheme clusters depending on requirements.

5. Optimize and discuss trade-offs

If the list is large, consider preprocessing the list into sets or using bitmasks for ASCII. Discuss time/space trade-offs and potential parallelization.

Key Points to Mention

  • Time complexity: O(n * m) with early exit, but can be optimized with preprocessing.
  • Space complexity: O(k) where k is number of unique characters in reference string.
  • Case sensitivity: use case folding (e.g., lower()) for case-insensitive comparison.
  • Unicode normalization: use NFC or NFD to handle equivalent characters.
  • Edge cases: empty reference string, empty strings in list, non-BMP characters.
  • Alternative approaches: bitmask for ASCII, regex, or using sets for each string.

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