Character AI·Software Engineer·Technical Phone Screen·Intermediate

IntermediateRejected

Jun 2026

Summary

Phone screen for a Software Engineer role at Character AI. It was mostly a coding round with LeetCode 76 (Minimum Window Substring), and the interviewer kept pushing for optimizations after each working solution. Wrote clean code, thought it went fine, then got rejected the next day with the vaguest possible feedback.

Questions Asked (1)

Implement the Minimum Window Substring algorithm (find the smallest substring of a source string that contains all characters of a target string), then iteratively optimize your solution for space and time efficiency.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I got this working pretty quickly with two hash maps and a helper that checked if the window was valid.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then present a brute-force solution to establish a baseline. Introduce the sliding window technique with frequency maps to achieve O(n) time, and discuss further optimizations such as using arrays instead of hash maps and early termination. Emphasize trade-offs between time and space, and test with edge cases.

Pro tip: Demonstrate awareness of real-world constraints by mentioning that the sliding window approach is optimal for large inputs, and that using fixed-size arrays (e.g., of size 128 for ASCII) can significantly reduce constant factors and memory overhead compared to hash maps.

1. Clarify and Understand

Ask clarifying questions about character set (ASCII/Unicode), case sensitivity, and whether the target can have duplicates. Confirm that the goal is to find the minimum length substring containing all characters of the target, including duplicates.

2. Brute Force Baseline

Describe a naive O(n^2 * m) approach: for each starting index, expand the window until all target characters are found, then record the minimum. This sets the stage for optimization.

3. Sliding Window with Frequency Maps

Explain the O(n) sliding window technique: use two pointers (left, right) and a frequency map for the target. Expand right to include characters, and when the window is valid, shrink left to find the minimum. Track the minimum window length and start index.

4. Optimize Space and Time

Replace hash maps with fixed-size arrays (e.g., int[128] for ASCII) to reduce overhead. Use a 'formed' counter to track how many target characters have been satisfied, avoiding full map comparisons. Discuss early termination when the window length equals the target length.

5. Analyze Trade-offs and Edge Cases

Compare time and space complexity of each approach. Discuss handling of edge cases: empty strings, target longer than source, no valid window, and Unicode characters. Mention that the sliding window is optimal for time, but space can be further reduced if the character set is small.

Key Points to Mention

Sliding window technique with two pointers and a frequency map.
Time complexity: O(n) for sliding window vs O(n^2) for brute force.
Space optimization: using fixed-size arrays instead of hash maps for known character sets.
The 'formed' counter to track valid windows without comparing entire maps.
Handling duplicates in the target string (e.g., target 'AABC' requires two 'A's).
Edge cases: empty source or target, no valid window, and Unicode support.

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