Explain Transformer and MoE Fundamentals
Company: Amazon
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
Overview: This question evaluates a candidate's conceptual mastery of modern deep-learning internals for large-language models, including distinctions between training and inference, Transformer architecture and data flow, the role of feed-forward networks, self-attention weighting, and mixture-of-experts (MoE) mechanisms, testing competencies in model internals, representation learning, and optimization within the Machine Learning domain. It is commonly asked to assess whether an interviewee can reason about mechanisms, trade-offs, and where computation and parameter updates occur (conceptual understanding rather than coding or implementation), with emphasis on how information is routed and transformed in Transformer blocks and why different components are needed.
