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Netflix·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Netflix coding screen for a software engineer role. One algorithmic problem, felt pretty focused on sliding window logic with a twist around episode grouping.

Questions Asked (1)

Q1

Given a list of (episode_id, day) pairs, an integer K, and an integer T, return True if any K-day sliding window contains at least two episodes whose IDs differ by at most T. Otherwise return False.

Algorithms & Data Structures
Author's notes

The sliding window part clicked pretty fast but the 'same season' condition tripped me up for a bit.

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AI HintsAI Generated

Suggested Approach

Sort the episodes by day, then use a sliding window of size K days to maintain a set of episode IDs. For each window, check if any two IDs differ by at most T by using a balanced BST or sorted list to find the nearest ID to each new ID.

Pro tip: Clarify whether the window is inclusive of both endpoints and whether episodes can have the same ID. Also, consider edge cases like K=1 or T=0.

1. Understand the problem

Restate the problem in your own words and ask clarifying questions about window definition, episode ID uniqueness, and constraints.

2. Choose data structures

Decide on a sliding window approach with a data structure that supports efficient insertion, deletion, and nearest neighbor queries, such as a balanced BST or a sorted list with binary search.

3. Design the algorithm

Outline the steps: sort episodes by day, initialize window, iterate through episodes, add new episode, check for close IDs, remove old episodes, and slide window.

4. Analyze complexity

Discuss time and space complexity, aiming for O(N log N) time and O(K) space, and consider if a more efficient approach exists.

5. Test with examples

Walk through a few test cases, including edge cases, to verify correctness and handle boundary conditions.

Key Points to Mention

  • Sliding window technique for fixed-size windows
  • Sorting episodes by day to process in chronological order
  • Using a balanced BST (e.g., TreeSet in Java) for O(log K) insert, delete, and nearest neighbor queries
  • Checking both floor and ceiling of the new episode ID to find if any existing ID is within T
  • Handling duplicates and ensuring window size exactly K days
  • Time complexity O(N log K) and space complexity O(K)

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