Pinterest·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

One coding question for a Pinterest MLE round, sliding window stuff. Pretty short session, nothing else to report.

Questions Asked (1)

Given an array and a window of size k, check whether each window's values form a pattern that decreases from the current element toward both ends (like a peak in the middle).

Algorithms & Data Structures

Author's notes

Took me a minute to even visualize what they were asking.

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AI HintsAI Generated

Suggested Approach

Clarify the problem and edge cases, then propose an efficient sliding window solution that checks the peak condition for each window in O(k) time, leading to O(n) overall. Discuss trade-offs and potential optimizations, and relate the problem to real-world ML scenarios like time-series analysis.

Pro tip: Demonstrate awareness of edge cases (e.g., k=1, k=2, all equal elements) and mention that the pattern is essentially a 'mountain' or 'bitonic' sequence. Also, connect the problem to detecting peaks in ML feature windows, showing domain relevance.

1. Clarify the problem

Ask clarifying questions to confirm the definition: Does 'decreases from the current element toward both ends' mean the window must be strictly decreasing from the peak to both ends? What about equal elements? What should be returned (boolean per window, indices, etc.)?

2. Outline a brute-force approach

For each window, find the maximum element and verify that the sequence strictly decreases as you move away from it in both directions. This takes O(n*k) time, which is acceptable for small inputs but not optimal.

3. Optimize with sliding window

Use a deque or two pointers to maintain the window and efficiently check the peak condition. For each window, you can check if it's bitonic by scanning from the peak outward, but since the window slides, you can update the check incrementally.

4. Analyze complexity and edge cases

The optimized solution runs in O(n) time and O(k) space. Discuss edge cases: k=1 (always true), k=2 (true if elements are not equal? Actually for k=2, any two distinct elements form a valid pattern? Need to define), all equal elements (false if strict decrease required), and windows at boundaries.

5. Relate to ML context

Mention how this pattern detection could be used in ML for feature engineering, such as identifying peaks in time-series data or validating windowed statistics, which is relevant to Pinterest's recommendation systems.

Key Points to Mention

Definition of the pattern: a bitonic (mountain) sequence that strictly decreases from the peak to both ends.
Sliding window technique to achieve O(n) time complexity.
Edge cases: k=1, k=2, all equal elements, and windows with multiple peaks.
Use of a deque or two pointers to efficiently track the peak and validate the pattern.
Trade-offs between brute-force and optimized solutions, and when to use each.
Real-world application in ML: detecting peaks in time-series or feature windows for anomaly detection or trend analysis.

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