Amazon Internship interview questions

During the interview, the interviewer fired off a series of tough behavioral questions that probed my past challenges and decision‑making—questions that, given their depth, easily consumed several minutes each—before pivoting to core machine‑learning concepts like contrastive learning, where they quizzed me on how I’d construct anchor‑positive‑negative pairs, choose an appropriate loss (e.g. InfoNCE versus triplet loss), and leverage large batch or memory‑bank strategies to learn robust embeddings; finally, they pressed me on post‑training techniques for LLMs, asking how I’d apply methods such as fine‑tuning, parameter‑efficient adapters like LoRA, low‑bit quantization, pruning, distillation, and even RLHF to both shrink model size and align outputs with human preferences—all delivered with a somewhat challenging tone that tested not only my technical knowledge but also my ability to stay calm, structured, and concise under pressure.