The Prep Dossier ML / AI Interview Reading List

What to read before
you interview.

One prioritized reading plan, built from 12 well-known interview guides. Items are grouped into tiers by how many sources back them. Check things off; your progress saves on this device.

Tip: tick items as you read. Progress is saved in this browser only, nothing is sent anywhere. Compiled July 2026 from public guides and updated over time; links may drift.
Synthesized from 12 sources  ·  2 tracks: MLE + Research  ·  22 items
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The order

Minimum 1 month · 2 months if full-time
Weeks 1–2

Fundamentals refresh (100-page book / StatQuest / cheat sheet) + start daily LeetCode (Blind 75).

Weeks 2–4

ML coding (implement a Transformer, Deep-ML) + a system-design framework and 3–4 case studies.

Ongoing

Write behavioral stories in STAR. Research track: build your job talk early and iterate on feedback.

Before loop

Do 3–5 mock interviews. Use an LLM as a mock interviewer by pasting the job description.

What they all agreed on

Habits · not reading, but they matter
  • Talk out loud while coding. Never code in silence.
  • Cap ~15–20 min per problem, then read the solution and move on.
  • One interview per day max — you fade by the third.
  • Start with lower-priority companies to warm up, save top choices for later.
  • Cluster offers in one window for negotiation leverage. Negotiation is worth weeks of effort.
Appendix

The 12 sources

Every recommendation above traces back to these. MLE applied-ML focus · RES research / PhD focus · REF reference only.