Machine Learning Interview Guide

Machine learning interviews assess algorithm knowledge, system design capability, and the ability to explain trade-offs under uncertainty. This guide covers the core topics.

Interview Types

ML System Design

ML system design interviews present problems such as "design a recommendation system for Netflix" and require a complete system architecture within 45-60 minutes. The scope includes data pipelines, model selection, serving infrastructure, metrics, and monitoring.

Resources:

• Introduction to ML System Design
• How to Answer ML System Design Questions
• ML Interview Rubric
• Data Processing Pipelines
• Model Selection
• Model Evaluation
• Model Deployment

Practice Problems

Example system design problems:

• Design Instagram Ranking Model
• Design Spotify Recommendation System
• Design Netflix Watch Prediction
• Design Bot Detection System
• Design Fraud Detection Model
• Design YouTube Video Prediction
• Design ETA Prediction System
• Design E-commerce Recommendation System

ML Fundamentals

Core concepts that appear in system design discussions and dedicated concept interviews:

• Linear Regression
• Logistic Regression
• Decision Trees
• Neural Networks
• Bias-Variance Tradeoff
• Feature Engineering

Interview Loop Structure

Round	Duration	Focus
ML System Design	45-60 min	End-to-end ML system architecture
ML Coding	45-60 min	Algorithm implementation
ML Concepts	30-45 min	Theory and applied ML knowledge
Coding	45-60 min	General algorithms and data structures
Behavioral	30-45 min	Past experiences and collaboration

Evaluation Criteria

Technical Depth

Interviewers assess understanding of why approaches work, not pattern memorization. This includes knowing when gradient boosting outperforms deep learning and why offline performance may not translate to production.

System Thinking

Model development represents approximately 20% of ML system work. The remaining components include data pipelines, feature engineering, serving infrastructure, and monitoring.

Trade-off Analysis

ML systems involve trade-offs between accuracy, latency, cost, and complexity. Candidates should explore multiple options and justify their choices.

Preparation Guidelines

1. Algorithm knowledge - Understand when to use each approach and common failure modes
2. Verbal practice - Practice system design explanations with time constraints
3. Metrics - Know precision, recall, AUC, and when to apply each
4. Production considerations - Include monitoring, A/B testing, and failure modes in designs
5. Examples - Prepare to discuss ML systems previously built or studied