AI System Design Interview Preparation
Interview prep for senior and staff AI engineering roles: 128 system design questions, answer frameworks with a worked mock-interview transcript, common pitfalls, nine whiteboard exercises, behavioral prep, a quick-answer FAQ, and August 2026 hiring trends.
What's new (August 2026): the question bank gained six August-2026 questions (rebuilding cost models after the first major inference price increase, capability-tiered access control, coding-agent supply-chain defense after the Shai-Hulud worm, reviewing agent plugins, securing a multi-tenant MCP server on the stateless spec, and shipping synthetic-content provenance now that EU and California transparency rules are enforceable) and now runs continuously Q1-Q128.
Before You Start
This folder assumes you already write production code and know LLM basics (tokens, context windows, embeddings, what RAG is). If those are shaky, spend a week in 01-foundations and the Courses guide first; interview prep on top of missing fundamentals produces fluent-sounding wrong answers, which is the worst possible outcome in a senior loop.
The files in this folder are designed to be read in order. Each builds on the last: questions teach the surface area, frameworks teach how to structure answers, pitfalls teach what kills offers, exercises rehearse the motion, behavioral covers the staff-level signal, and job-market trends cover the current hiring landscape.
Read in Order
Role-Specific Prep Paths
Files in This Folder
| File | Purpose |
|---|---|
| 01-question-bank.md | 128 real interview questions (Q1-Q128, continuously numbered) grouped by topic, with model answers and follow-ups (through August 2026). |
| 02-answer-frameworks.md | Five structured answer frameworks (SPIDER, ETA, tradeoff, debugging, STAR-L) plus a worked 45-minute SPIDER mock-interview transcript. |
| 03-common-pitfalls.md | Patterns that kill staff-level offers: hand-waving on tradeoffs, missing observability, ignoring failure modes. |
| 04-whiteboard-exercises.md | Nine system design exercises with worked solutions, including evaluation pipeline design and agent memory. The closest simulation of a real loop. |
| 05-behavioral-for-ai-roles.md | Behavioral prep for AI-specific scenarios with six worked STAR-L examples, compensation and leveling questions, and an out-loud practice guide. |
| 06-job-market-trends-2026.md | Role taxonomy, comp ranges, interview process patterns, and emerging titles (FDE, AI Eval Engineer, AI Reliability Engineer, MCP Engineer). |
| 07-faq.md | Short, direct answers to the most-asked questions about AI engineering, RAG, agents, models, eval, inference, memory, and security. Useful for quick reference and for newcomers to the field. |
Companion Resources
- Role Transition Guide for prepping from backend, frontend, QA, PM, or EM into AI.
- Recommended Courses for foundational learning before interview prep.
- Glossary for quick term definitions during prep.
- Case Studies for production architectures that map directly to whiteboard prompts.
Key Takeaways
- The files are designed to be read in order; jumping straight to questions without absorbing answer frameworks leaves answers unstructured.
- Whiteboard exercises (file 04) are the closest simulation to real interviews; do at least three before any loop.
- Behavioral prep (file 05) is what separates staff candidates from senior candidates; do not skip it.
- The August 2026 job market chapter (file 06) is a moat: candidates who know the hiring landscape can ask better questions and tailor stories.
- Recheck this folder monthly; new question batches are added as hiring trends shift.