Cracking AI Engineering Job Interviews

29 lessons
6 modules
Cracking AI Engineering Job Interviews

About This Course

Stop Memorizing Algorithms. Start Designing Systems.

Most candidates fail AI system design interviews because they treat them like coding problems. To land a role at Meta, Google, or a top startup, you need to think like a technical leader.

Master the 3×3 Method—the framework that will help you transform vague prompts into production-ready architectures.

What you’ll master in weeks, not months:

  • THINK Like a Senior Engineer: Frame problems using product logic and business metrics, not just code.

  • BUILD Scalable Systems: Architect 25 real-world scenarios, including Recommendation Engines, Enterprise RAG, and Fraud Detection.

  • DEFEND Your Decisions: Demonstrate maturity by justifying trade-offs in latency, cost, fairness, and safety.

How the lessons are structured:

  • Each of the 25 questions below is self-contained and includes all the definitions you need to understand the lesson.

  • Where helpful, you’ll also find small prototype code snippets. You won’t use this code in an interview, but it will deepen your understanding of the concept—so you can answer with more confidence.

Don't just pass the interview. Prove you’re ready to lead.

Course Curriculum

1

Introduction to AI ML System Design

The 3×3 Method transforms overwhelming AI interviews into a manageable nine-step process. Named for its three phases—each containing three steps—this framework mirrors how smart engineers actually build production systems: Think (frame the problem), Build (architect the solution), and Defend (own the lifecycle). Unlike scattered checklists, the 3×3 structure reduces cognitive load under pressure by grouping related tasks into logical blocks. If you forget a detail, simply move to the next phase. This isn't a rigid script—it's a flexible mental map that lets you lead the conversation confidently, demonstrating the structured thinking, proactive scoping, and trade-off analysis that distinguish great engineers from good ones at top tech companies.

Lessons (4)

The 3×3 Method: A Framework for AI System Design Interviews
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Phase 1 : THINK - Frame the problem intelligently
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Phase 2 : BUILD - Architect the AI System
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Phase 3 : DEFEND - Evaluate, Iterate & Communicate
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2

Ranking & Recommendation Systems

Lessons (7)

Q1 Design a personalized news feed ranking system for Facebook/Instagram serving billions of users
Q2 Design a large-scale recommendation system for Netflix that balances personalization with content diversity
Q3 Design a ranking system for TikTok’s “For You” feed optimized for engagement and long-term retention
Q4 Design a recommendation engine for LinkedIn job postings that matches candidates to jobs fairly
Q5 Design a recommendation engine for Spotify that balances personalization with discovery of new artists
Q6 Design a marketplace ranking system for Airbnb that balances host satisfaction, guest conversion, and fairness
Q7 Design a system that generates and ranks personalized learning content for an edtech platform like Coursera
3

Search, RAG & Multi-Modal Systems

Lessons (6)

Q8 Design a retrieval-augmented generation (RAG) system for enterprise search across millions of documents
Q9 Design a large-scale multi-modal search system (text, images, video) for e-commerce like Amazon
Q10 Design a large-scale vector search system for similarity search across billions of images
Q11 Design a conversational assistant (like ChatGPT) for customer support with retrieval + action execution
Q12 Design a healthcare diagnostic assistant that integrates structured patient data with LLM-based reasoning
Q13 Design a large-scale real-time translation system for cross-lingual messaging at WhatsApp
4

Trust, Safety & Red Teaming

Lessons (5)

Q14 Design a system to detect abusive or harmful content (hate speech, misinformation) on Twitter/YouTube
Q15 Design a system to detect and prevent collusion or cheating in online multiplayer games
Q16 Design a system for autonomous vehicles that detects adversarial attacks on perception models
Q17 Design a red-teaming system that automatically probes LLMs for jailbreaks, prompt injections, and unsafe outputs
Q18 Design a content moderation pipeline for real-time livestream platforms like Twitch
5

Financial, Fraud & Anomaly Detection

Lessons (3)

Q19 Design a fraud detection system for Stripe/PayPal that minimizes both false positives and false negatives
Q20 Design an anomaly detection system for financial transactions in a bank with millions of daily transfers
Q21 Design an ad ranking framework for Google Ads that optimizes for revenue while preserving user trust
6

ML Ops, Evaluation & Large-Scale Deployment

Lessons (4)

Q22 Design a spam detection system for Gmail that scales to billions of emails per day
Q23 Design a large-scale A/B testing framework for ML models at a company like Meta or Google
Q24 Design a system to automatically evaluate the safety and robustness of new ML models before deployment
Q25 Design a large-scale voice recognition and transcription system for real-time meetings (e.g., Zoom)

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