What problem does it solve? Interviewers and mock-interview systems often lack structured, role-specific question banks for algorithm engineering positions, leading to shallow questions that only test memorized definitions rather than real understanding. ## Core Features & Use Cases - Domain Question Bank: Covers machine learning fundamentals, deep learning training, Transformer architecture, LLM fine-tuning, recommendation systems, feature engineering, AB testing, model deployment, data quality, and CV/NLP specializations. - Difficulty Ladders and Signals: Each topic provides a progression ladder from basic to advanced, plus danger signals and expected signals to evaluate candidate answers. - Resume-Based Probing: Hooks map resume claims (e.g., "accuracy improved X%", competition rankings, model deployment) to targeted follow-up questions on experiment validity, leakage, baselines, and online metrics. - Use Case: An AI mock-interview system loads this Skill when a candidate applies for a recommendation algorithm role, then generates questions about recall sample construction, negative sampling, and AB test confidence tailored to the candidate's resume. ## Quick Start Load this Skill and ask it to generate a mock interview question set for a machine learning engineer candidate based on their resume.
