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This is a Medium difficulty Data Manipulation (SQL/Python) question, commonly asked during Onsite rounds at OpenAI.

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This question is commonly asked for Machine Learning Engineer candidates at OpenAI during technical interviews.

Implement vectorized NumPy ops and explain broadcasting

Last updated: May 27, 2026

Quick Overview

This question evaluates proficiency in vectorized numerical computing with NumPy, understanding of broadcasting semantics, and awareness of numerical stability and algorithmic time/space complexity when manipulating high-dimensional arrays.

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Implement vectorized NumPy ops and explain broadcasting

OpenAI

Aug 11, 2025, 12:00 AM

MediumMachine Learning EngineerOnsiteData Manipulation (SQL/Python)

Implement vectorized NumPy code for: (a) computing pairwise cosine similarity between two real-valued matrices X (shape n×d) and Y (shape m×d) without explicit Python loops; (b) computing a numerically stable softmax for a 2D array along the last axis; (c) explaining how broadcasting works if X has shape (n, 1, d) and Y has shape (1, m, d). Analyze time and space complexity, and discuss pitfalls such as unintended broadcasting, dtype issues, and memory usage.

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