Quick Overview

This coding question tests a machine learning engineer's understanding of Transformer internals by requiring a from-scratch NumPy implementation of multi-head scaled dot-product attention. It evaluates mastery of linear projections, head splitting, numerically stable softmax, and causal masking — key competencies for ML roles involving large language models.

Implement Multi-Head Attention from Scratch in NumPy

Company: Amazon

Role: Applied Scientist

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Onsite

Overview: This coding question tests a machine learning engineer's understanding of Transformer internals by requiring a from-scratch NumPy implementation of multi-head scaled dot-product attention. It evaluates mastery of linear projections, head splitting, numerically stable softmax, and causal masking — key competencies for ML roles involving large language models.

Read the full Amazon Applied Scientist interview experience this question came from

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