01Curated Problem ListsStructured problem lists for AI/ML interview prep - the Core 50, role-specific, and company-style.02The Core 50The 50 most important problems every AI/ML candidate should master.03MLE Problem ListCurated problems for Machine Learning Engineer interviews - coding, ML, and system design.04AI Engineer Problem ListCurated problems for AI Engineer interviews - LLMs, RAG, agents, and production systems.05Data Scientist Problem ListCurated problems for Data Scientist interviews - statistics, experimentation, and modeling.06MLOps Problem ListCurated problems for MLOps interviews - infrastructure, pipelines, and reliability.07Research Engineer Problem ListCurated problems for Research Engineer interviews - implementations, math, and paper discussion.08Data Engineer Problem ListCurated problems for Data Engineer interviews - SQL, pipelines, and data modeling.09Easy Tier ProblemsWarm-up problems for building confidence - fundamentals across all categories.10Medium Tier ProblemsCore difficulty problems - the backbone of interview preparation.11Hard Tier ProblemsAdvanced problems for top-tier preparation - Staff+ and research roles.12Google-Style ProblemsProblems that match Google's interview style - breadth, scale, and Googleyness.13Meta-Style ProblemsProblems that match Meta's interview style - system design and ML depth.14Startup-Style ProblemsProblems that match startup interview styles - practical, end-to-end, and fast-paced.
