Google·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Google ML Engineer interview with a conveyor belt / streaming data problem. The core challenge was extending a sorted-window approach to handle items arriving in arbitrary order, which made it more interesting than the warmup version.

Questions Asked (1)

Items arrive one at a time on a conveyor belt, each with a numeric size. Given a threshold T, detect and return any triple of items whose max minus min is less than T, as soon as such a triple exists in the arrival history. Items arrive in arbitrary order. How do you solve this incrementally?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The part 1 warmup (sorted arrivals) felt manageable since you just check the last three items each time.

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AI HintsAI Generated

Suggested Approach

Maintain a sorted data structure of the items seen so far, and after each insertion check the window of three consecutive items around the new item to see if any triple satisfies the condition. Since any valid triple must be three consecutive items in sorted order, this check is sufficient and can be done in O(log n) time per insertion using a balanced BST or a skip list.

Pro tip: Emphasize that the condition is equivalent to finding three items within a range of T, so sorting is key; also note that if the stream is bounded or known, a bucket-based approach could give O(1) amortized time, but a balanced BST is more general and robust.

1. Understand the problem and condition

Restate the problem: we need to detect any triple (a,b,c) from the arrival history such that max(a,b,c) - min(a,b,c) < T. Recognize that this is equivalent to finding three items that lie within an interval of length T.

2. Identify the key insight

Prove that if such a triple exists, then in the sorted order of all items seen so far, there must be three consecutive items that satisfy the condition. This reduces the problem to checking only local windows around each newly inserted item.

3. Choose the data structure

Select a balanced binary search tree (e.g., Red-Black Tree) or a skip list to maintain the sorted order of items, supporting insertion and neighbor queries in O(log n) time. Alternatively, if the value range is small, consider a bucket-based approach for O(1) amortized operations.

4. Design the incremental algorithm

Upon each new item, insert it into the data structure, then retrieve its immediate predecessor and successor (and possibly the next successor) to form candidate triples. Check if any of these triples satisfy the condition; if so, return it immediately.

5. Analyze complexity and trade-offs

State that each insertion and neighbor lookup takes O(log n) time, so the overall time per item is O(log n). Discuss space O(n) for storing all items. Mention that early termination is possible as soon as a valid triple is found.

Key Points to Mention

The condition max - min < T is equivalent to all three items lying within an interval of length T.
In sorted order, any valid triple must be three consecutive items; thus checking local windows suffices.
Use a balanced BST (or skip list) to maintain sorted order and support O(log n) insertion and neighbor queries.
After inserting a new item, check triples involving the new item and its immediate neighbors (predecessor, successor, and possibly second predecessor/successor).
Time complexity: O(log n) per item; space complexity: O(n).
If the value range is bounded, a bucket-based approach can achieve O(1) amortized time per item.

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