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Snapchat·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Snapchat ML engineer interview that came down to a classic k-sum coding problem. Nothing too exotic, but the edge cases around duplicates are where things get slippery.

Questions Asked (1)

Q1

Given an integer array and a target value, find all unique quadruplets of elements that sum to the target.

Algorithms & Data Structures
Author's notes

The core idea isn't hard once you've done 3Sum before.

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AI HintsAI Generated

Suggested Approach

Start by clarifying constraints (array size, value ranges, duplicates) and expected output format. Then present a solution that sorts the array and uses a two-pointer technique within nested loops to efficiently find unique quadruplets, discussing time and space complexity. Optionally, mention alternative approaches like hashing and compare trade-offs.

Pro tip: Demonstrate awareness of duplicate handling by explaining how sorting and skipping repeated elements ensures uniqueness, and proactively discuss how you would scale the solution for large datasets, which is crucial at Snapchat.

1. Clarify requirements and constraints

Ask about input size, value ranges, whether the array can contain duplicates, and if the output should be sorted or in any order. Confirm the expected time/space complexity.

2. Outline a brute-force approach and its limitations

Mention the naive O(n^4) solution using four nested loops and explain why it's inefficient for large inputs, setting the stage for optimization.

3. Propose an optimized two-pointer approach

Describe sorting the array, then using two nested loops for the first two numbers and a two-pointer technique for the remaining two, skipping duplicates to ensure unique quadruplets.

4. Analyze complexity and edge cases

State the time complexity O(n^3) and space complexity O(1) or O(n) depending on sorting, and discuss edge cases like empty array, no solution, and multiple duplicates.

5. Discuss alternative approaches and trade-offs

Briefly mention hashing-based solutions (e.g., using a hash map to store pair sums) and compare their trade-offs in terms of time, space, and implementation complexity.

Key Points to Mention

  • Sorting the array to enable two-pointer technique and handle duplicates
  • Using two nested loops for the first two elements and two pointers for the last two
  • Skipping duplicate elements to avoid duplicate quadruplets
  • Time complexity O(n^3) and space complexity O(1) or O(n) due to sorting
  • Edge cases: array size less than 4, no valid quadruplets, integer overflow
  • Alternative hashing approach with O(n^2) average time but O(n^2) space

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