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Netflix·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Netflix coding screen, one algorithmic problem, pretty standard stuff. The sliding window thing is a classic but I still managed to fumble the edge cases a bit before getting it sorted.

Questions Asked (1)

Q1

Given a string, find the length of the longest substring with no repeated characters.

Algorithms & Data Structures
Author's notes

Knew immediately it was a sliding window problem, which felt good.

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AI HintsAI Generated

Suggested Approach

Use a sliding window with two pointers to maintain a window of unique characters, expanding the right pointer and shrinking the left when a duplicate is found. Track the maximum window length seen. This yields O(n) time and O(min(n, alphabet)) space.

Pro tip: Clarify assumptions upfront (e.g., ASCII vs Unicode, empty string, case sensitivity) and discuss trade-offs between the optimal sliding window and simpler brute-force approaches to show engineering maturity.

1. Clarify requirements and edge cases

Ask about character set (ASCII/Unicode), case sensitivity, and expected input size. Confirm behavior for empty strings and strings with all unique characters.

2. Outline brute-force baseline

Mention that checking all substrings for uniqueness takes O(n^3) or O(n^2) with a set, establishing a baseline before optimizing.

3. Design sliding window solution

Use two pointers (left, right) and a hash map/set to track characters in the current window. Expand right, and when a duplicate is found, move left past the previous occurrence.

4. Analyze complexity and optimize

Explain that each character is visited at most twice, giving O(n) time. Space is O(min(n, alphabet size)). Optionally, use an array for fixed ASCII to improve constant factors.

5. Test with examples and edge cases

Walk through examples like 'abcabcbb' (3), 'bbbbb' (1), 'pwwkew' (3), and empty string (0) to verify correctness.

Key Points to Mention

  • Sliding window technique with two pointers
  • Hash map/set to track last seen index of characters
  • Time complexity O(n) and space complexity O(min(n, alphabet))
  • Handling edge cases: empty string, all same characters, all unique characters
  • Trade-offs between brute-force and optimized approaches
  • Potential optimization using fixed-size array for ASCII characters

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