Google·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Google ML engineer phone screen, one coding problem the whole time. The problem was more interesting than I expected but I second-guessed my data structure choice for way too long.

Questions Asked (1)

Design a class for a conveyor belt that ingests items one at a time by size. Implement an add(size) method that, as soon as any three recorded items form a group where the difference between the largest and smallest is below a given threshold, returns that triple.

Algorithms & Data StructuresSystem Design

Author's notes

I kept the items in a sorted list using bisect insertion, so each add is O(log n).

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AI HintsAI Generated

Suggested Approach

Clarify the problem requirements, then propose an efficient data structure like a balanced BST or sorted list to maintain items in sorted order. For each new item, check windows of three consecutive items in the sorted order to see if the difference between max and min is below the threshold, returning the first such triple.

Pro tip: Mention that checking only consecutive triples in sorted order is sufficient because any valid triple must have its max and min within a window of three consecutive elements. This shows deep insight and avoids unnecessary complexity.

1. Clarify Requirements

Ask about input constraints, threshold definition, and whether the triple should be returned immediately or if all triples need to be found. Confirm if items are ingested one at a time and if duplicates are allowed.

2. Choose Data Structure

Select a data structure that maintains sorted order and supports efficient insertion and neighbor queries, such as a balanced binary search tree (e.g., TreeSet in Java) or a skip list.

3. Design Algorithm

On each add(size), insert the new size into the sorted structure. Then check the new item's immediate neighbors (up to two on each side) to form all possible consecutive triples involving the new item. For each triple, compute max - min and compare to threshold.

4. Return and Optimize

If a valid triple is found, return it immediately. Discuss time complexity: O(log n) per insertion and O(1) checks per insertion, leading to O(n log n) overall. Mention potential optimizations like early termination.

5. Handle Edge Cases

Address cases with fewer than three items, duplicate sizes, and threshold boundaries. Ensure the solution works for large streams and discuss memory considerations.

Key Points to Mention

Sorted data structure (e.g., balanced BST) for efficient insertion and neighbor access.
Checking only consecutive triples in sorted order is sufficient to find any valid triple.
Time complexity: O(log n) per insertion, O(n log n) total; space complexity O(n).
Immediate return upon finding a valid triple, as per problem statement.
Handling duplicates and threshold inclusivity (difference < threshold vs <=).
Potential alternative: using a sliding window if items were already sorted, but not applicable for streaming.

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