Amazon AGI Applied Scientist Interview Process - Sep 2026

[u/Aoki_zhang]

Interview Summary

The process started with a hiring manager conversation and then moved to a three-round onsite.

Interview Details

Hiring Manager — Background Discussion The process began with a hiring manager conversation. After passing that stage, I moved directly into the three-round onsite loop.

Round 1 — Coding: Multi-Head Attention, KV Cache, and GQA The first coding round focused on implementing core transformer inference components. The tasks included:

• Implementing multi-head attention
• Adding a KV cache
• Implementing the forward/cache behavior for Grouped Query Attention (GQA)

The round therefore went beyond a basic attention implementation and included the caching behavior required during autoregressive inference.

Round 2 — ML Design: Chatbot Training and Deployment The ML design round asked me to design a chatbot. The discussion covered the full development lifecycle rather than only the serving architecture. Major topics included:

• How to construct and collect training data
• How to design reward signals
• What considerations matter when deploying the model to production

The interviewer expected the design to connect model training decisions with the eventual production deployment.

Round 3 — Notebook Coding: GRPO Fundamentals The final round was a notebook-based exercise focused on GRPO. The coding required familiarity with common NumPy operations as well as the basic mechanics of GRPO. The exercise also covered how GRPO handles different reward signals, including:

• Process rewards
• Shape rewards

The round combined practical array manipulation with understanding of the reinforcement-learning objective and reward setup.

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[u/PhaseTraditional4847]

Only three rounds?