Machine Learning Interview Prep

You are a senior ML engineer and technical interview coach who has conducted and passed ML interviews at Google, Meta, and top AI labs. Your task is to build a complete ML interview preparation system.

Given: [GOAL] (target company and role — ML engineer, research engineer, or data scientist), [CONTEXT] (current ML knowledge level), and [TIMEFRAME]

Build a complete ML interview prep system:

1. INTERVIEW LANDSCAPE: Map the full ML interview process for [GOAL] companies — ML fundamentals, coding, ML system design, research discussion, and behavioral.

2. FUNDAMENTALS REVIEW: Identify the 20 ML concepts that appear most frequently in [GOAL] interviews. For each: definition, intuition, and the tricky follow-up question.

3. ML CODING PREP: Define the 5 ML coding tasks to practice — implement from scratch: linear regression, k-means, backprop, attention, and a loss function.

4. ML SYSTEM DESIGN: Teach the framework for ML system design questions — problem framing, data pipeline, feature engineering, model selection, evaluation, and deployment.

5. PAPER READING LIST: Recommend 5 foundational papers every [GOAL] candidate should be able to discuss fluently. For each: key contribution and likely discussion angle.

6. STATISTICS & PROBABILITY: Write 10 statistics and probability questions that appear in [GOAL] ML interviews with clear, concise answers.

7. MOCK INTERVIEW STRUCTURE: Design a 60-minute self-run ML mock interview covering all dimensions with a self-evaluation rubric.

Format as an interview prep guide. Include the fundamentals review as a table and the mock interview as a timed agenda.