Study Guide
Interview Prep Guide
Master AI/ML concepts and ace your technical interviews
Recommended Resources
Top resources to prepare for AI/ML interviews
🎓
Featured Course
AI Engineer Zero to Hero Crash Course
Complete AI/ML engineering course covering LLMs, RAG, Agents, MLOps, and more.
Books
- • Designing Machine Learning Systems
- • Hands-On Machine Learning
- • Deep Learning (Goodfellow)
- • NLP with Transformers
Courses
- • DeepLearning.AI Specializations
- • Fast.ai Practical Deep Learning
- • Stanford CS229, CS231n
- • Hugging Face NLP Course
Practice
- • LeetCode (ML/AI problems)
- • Kaggle Competitions
- • Papers with Code
- • GitHub ML Projects
Stay Updated
- • arXiv ML Papers
- • The Batch (Andrew Ng)
- • AI Twitter/X Communities
- • Towards Data Science
Key Topics to Study
Machine Learning Fundamentals
Core ML concepts every AI engineer must know
▼ Click to expand
Deep Learning Fundamentals
Neural networks and deep learning basics
▼ Click to expand
Transformer Architecture Deep Dive
Understanding the architecture behind LLMs
▼ Click to expand
RAG Systems Architecture
Building Retrieval Augmented Generation systems
▼ Click to expand
LLM Fine-tuning Techniques
Advanced techniques for customizing LLMs
▼ Click to expand
ML System Design
Designing production ML systems at scale
▼ Click to expand
Practice Questions
🧠 ML FundamentalsentryGlassdoor - Google ML Interview
Explain the difference between supervised and unsupervised learning. Give examples of each.
Interview Tips
- Start with clear definitions
- Give 2-3 concrete examples for each
- Mention when to use each approach
🧠 ML FundamentalsentryReddit r/cscareerquestions
What is overfitting and how do you prevent it?
Interview Tips
- Define overfitting clearly
- Mention multiple prevention techniques
- Discuss train/validation/test split
🔮 Deep LearningentryGlassdoor - Meta AI Interview
Explain how a neural network learns through backpropagation.
Interview Tips
- Walk through forward pass first
- Explain chain rule for gradients
- Mention optimizer role
🏗️ System DesignmidGlassdoor - Amazon ML Interview
How would you design a recommendation system for an e-commerce platform?
Interview Tips
- Clarify requirements first
- Discuss collaborative vs content-based
- Mention cold start problem
- Talk about evaluation metrics
🔮 Deep LearningmidReddit r/MachineLearning
Explain the attention mechanism in Transformers. Why is it important?
Interview Tips
- Start with the intuition
- Write out the formula
- Explain multi-head attention
- Compare to RNNs
📚 LLMsmidGlassdoor - Anthropic Interview
How would you evaluate a RAG system? What metrics would you use?
Interview Tips
- Separate retrieval and generation metrics
- Mention faithfulness and relevance
- Discuss human evaluation
🏗️ System DesignseniorGlassdoor - Stripe ML Interview
Design a real-time fraud detection system that handles millions of transactions per second.
Interview Tips
- Discuss feature engineering
- Balance latency vs accuracy
- Mention streaming architecture
- Talk about model updates
📚 LLMsseniorReddit r/LocalLLaMA
How would you fine-tune a large language model for a specific domain? Walk through your approach.
Interview Tips
- Discuss data collection strategy
- Compare full fine-tuning vs PEFT
- Mention evaluation approach
- Talk about deployment
📚 BehavioralseniorGlassdoor - OpenAI Interview
Tell me about a time you had to make a difficult technical decision with incomplete information.
Interview Tips
- Use STAR format
- Show analytical thinking
- Discuss risk assessment
- Mention outcome and learnings
🏗️ System DesignstaffGlassdoor - Netflix ML Platform Interview
How would you architect an AI platform that serves multiple ML teams across the organization?
Interview Tips
- Discuss abstraction layers
- Mention governance and compliance
- Talk about self-service vs managed
- Consider multi-tenancy
📚 AI SafetystaffGlassdoor - DeepMind Interview
How do you evaluate and mitigate risks when deploying a new AI system at scale?
Interview Tips
- Discuss failure modes
- Mention canary deployments
- Talk about monitoring
- Address ethical considerations
📚 LeadershipprincipalReddit r/ExperiencedDevs
Describe how you would lead the technical strategy for an AI-first product roadmap.
Interview Tips
- Show strategic thinking
- Balance innovation vs reliability
- Discuss stakeholder management
- Mention build vs buy decisions