Crack the reinforcement learning interview — by designing real systems, not memorizing equations.

RL interviews don't reward people who can recite the Bellman equation. They reward people who can formulate a problem as an MDP, choose the right algorithm, reason about a production system end-to-end, and know when not to use RL at all.

Reinforcement Learning System Design Interview takes you from first principles to frontier-lab topics, with a runnable Python lab and 17 sourced production case studies from Netflix, Microsoft, Uber, Meta, Google, Yahoo, and Expedia.

What's inside: the full RL curriculum (MDPs → DQN → PPO → SAC); RLHF, DPO, and GRPO for LLMs; offline RL, model-based RL, and advanced exploration; 150+ mock interview Q&A with detailed answers; 17 attributed case studies worked through a 10-step senior design checklist; two end-to-end system-design walkthroughs; a runnable code companion with reproducible multi-seed results; and a from-scratch derivations appendix.

Who it's for: ML Engineers, AI/Applied Scientists, RLHF/alignment and robotics candidates. Assumes supervised deep learning and Python; no prior RL coursework needed.

Stop memorizing algorithms. Start designing systems — and walk into your RL interview ready.