Explain NLP/RL concepts used in LLM agents
Company: Amazon
Role: Applied Scientist
Category: Machine Learning
Difficulty: hard
Interview Round: Onsite
Overview: This question evaluates proficiency in transformer-based NLP, embedding methods, LLM agent architecture and evaluation, retrieval techniques for RAG, and reinforcement learning fundamentals, testing understanding of model families, static vs contextual embeddings, agent components and metrics, lexical vs dense retrieval, BM25 concepts, and on/off-policy Q-learning. It is commonly asked to assess an applied Machine Learning engineer's ability to reason about trade-offs and design choices across Machine Learning, Natural Language Processing, Information Retrieval, and Reinforcement Learning, emphasizing both conceptual understanding and practical application.
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