Snapchat·Machine Learning Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026Remote

Summary

Snapchat ML engineer screen, one coding question the whole time. They wanted a specific heap-based solution and would push back if you went a different direction, so knowing your complexity tradeoffs cold really mattered here.

Questions Asked (1)

Given an array of 2D points and an integer k, return the k points closest to the origin. Walk through an efficient solution and explain your complexity reasoning.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The question itself is straightforward but they weren't just looking for any working solution.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: distance is squared Euclidean, and we need the k closest points. Then present a max-heap of size k as the optimal solution, walking through the algorithm and analyzing time and space complexity. Finally, discuss trade-offs with sorting and quickselect, and mention edge cases.

Pro tip: Emphasize that you avoid computing square roots by comparing squared distances, showing attention to performance. Also, relate the heap approach to real-time systems where k is small, which is common in ML pipelines.

1. Clarify the problem

Confirm that distance is Euclidean and that we need the k closest points, not sorted. Discuss tie-breaking and input constraints (e.g., k <= n).

2. Choose the optimal data structure

Explain that a max-heap of size k efficiently maintains the k smallest distances. For each point, compute squared distance and compare with the heap's max.

3. Walk through the algorithm

Iterate through points: if heap size < k, push; else if distance < heap max, pop and push. Finally, extract all points from the heap.

4. Analyze complexity

Time: O(n log k) due to heap operations; Space: O(k) for the heap. Compare with sorting O(n log n) and quickselect average O(n).

5. Discuss edge cases and trade-offs

Mention cases like k=0, k=n, duplicate points, and large n. Discuss when sorting or quickselect might be preferable (e.g., if k is close to n).

Key Points to Mention

Use squared Euclidean distance to avoid expensive square root operations.
Max-heap of size k gives O(n log k) time and O(k) space, optimal when k << n.
Alternative approaches: sorting (O(n log n)) and quickselect (average O(n)).
Trade-offs: heap is simple and works well for streaming data; quickselect modifies input and has worst-case O(n^2).
Edge cases: k=0, k=n, duplicate points, and points with same distance.
In ML contexts, this is similar to nearest neighbor search, and for large-scale data, approximate methods like LSH may be used.

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