Explain Transformers and MoE in LLMs
Company: Amazon
Role: Applied Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Onsite
You are interviewing for a role working with large language models (LLMs).
Explain the following concepts and how they relate to building and scaling LLMs:
1. **Transformer architecture**
- What are the key components (e.g., self-attention, multi-head attention, positional encodings, feed-forward networks)?
- How does the self-attention mechanism work at a high level?
- Why are Transformers well-suited for language modeling compared to RNNs/LSTMs?
2. **Mixture-of-Experts (MoE) architecture**
- What problem does MoE try to solve in the context of LLMs?
- How does expert routing work conceptually (e.g., gating networks, top-k experts)?
- What are the main trade-offs of MoE (compute efficiency vs. model complexity, training stability, load balancing)?
3. **Collective communication and parallelism for LLMs**
- Briefly describe common forms of parallelism used to train and serve large models: data parallelism, tensor/model parallelism, and pipeline parallelism.
- What is collective communication (e.g., all-reduce, all-gather, broadcast) and why is it critical for large-scale distributed training?
- Give a simple example of where an all-reduce operation is used when training a Transformer model.
Focus on clear explanations that would help a strong software engineer understand how large language models are structured and scaled.
Overview: This question evaluates understanding of large language model architectures and systems-level scaling competencies—specifically the Transformer core concepts, Mixture-of-Experts routing, and collective communication primitives—within the Machine Learning category.
Read the full Amazon Applied Scientist interview experience this question came from
