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Bloomberg·Software Engineer·Technical Phone Screen·Intermediate

IntermediateRejected
Jun 2026

Summary

Bloomberg SWE technical phone screen that went sideways fast. The interviewer pushed past the standard sliding window answer and wanted something else entirely, and I had nothing.

Questions Asked (1)

Q1

Given a string, find the length of the longest substring without repeating characters. Then explain an approach that does NOT use a sliding window or move a left pointer.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I nailed the sliding window version, complexity and all.

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AI HintsAI Generated

Suggested Approach

First, solve the problem using the standard sliding window approach, then propose an alternative that uses a hash map to store the last seen index of each character and a 'start' variable to track the beginning of the current substring, updating it when a repeat is found. Emphasize that this method avoids moving a left pointer explicitly by jumping the start to the last occurrence + 1.

Pro tip: Show awareness of trade-offs: the hash map approach is still O(n) but may use more memory; mention that the sliding window is more intuitive but the alternative demonstrates deeper understanding of the problem's invariants.

1. Clarify and Confirm

Restate the problem to ensure understanding: find the length of the longest substring without repeating characters. Ask about character set (ASCII vs Unicode) and edge cases (empty string, all unique).

2. Present the Standard Solution

Briefly explain the sliding window approach with two pointers and a set, noting its O(n) time and space complexity. This establishes a baseline.

3. Introduce the Alternative Approach

Describe a method using a hash map to store the last seen index of each character and a 'start' variable. When a repeat is found, update 'start' to max(start, last_seen[char] + 1) and update the max length.

4. Walk Through an Example

Trace the algorithm on a sample string like 'abcabcbb' to illustrate how 'start' jumps and how the max length is computed.

5. Analyze Complexity and Trade-offs

State that the alternative is also O(n) time and O(min(n, alphabet)) space. Compare with sliding window: both are efficient, but the hash map approach may be less intuitive yet avoids explicit left pointer movement.

Key Points to Mention

  • Hash map to store last seen index of each character
  • Start variable to track the beginning of the current substring
  • Update start to max(start, last_seen[char] + 1) when a repeat is found
  • Time complexity O(n) and space complexity O(min(n, alphabet))
  • Comparison with sliding window: both O(n), but different implementation details
  • Edge cases: empty string, single character, all repeating characters

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