Break Into AI

The most comprehensive AI/ML interview handbook - covering every role, every round, and every company.

What This Covers

Section	Focus
The AI Career Landscape	Role deep-dives, salary bands, career ladders
The Interview Process	Anatomy of AI interview loops
Resume & Portfolio	AI-optimized resume, GitHub portfolio, outreach
Coding Interviews	DSA + NumPy/Pandas/SQL for ML roles
ML Fundamentals	Bias-variance, loss functions, ensemble methods
Deep Learning	Backprop, CNNs, transformers, distributed training
LLM Interviews	Transformer internals, RAG, RLHF, agents
ML System Design	14 full design problems + framework
Paper Discussion	How to discuss Attention, BERT, GPT, LoRA
Behavioral	STAR for ML, project deep-dives, ethics
Take-Home Projects	What evaluators look for, templates
Company Guides	Google, Meta, OpenAI, Anthropic, and more
Negotiation & Offers	AI compensation, RSUs, startup equity
Curated Problem Lists	Core 50, role-specific, difficulty-tiered
Role-Specific Prep Paths	6–10 week guided plans per role

Target Roles

• Machine Learning Engineer - Build and deploy ML models at scale
• AI Engineer - Ship AI-powered products with LLMs and agents
• MLOps Engineer - Infrastructure, pipelines, and reliability for ML
• Data Scientist - Statistical modeling, experimentation, insights
• Research Engineer - Implement and scale research ideas
• Data Engineer - Data pipelines, warehousing, feature engineering

How to Use This Guide

1. Start with your target role → Jump to Role-Specific Prep Paths
2. Follow the prep plan → Each role has a 6–10 week structured schedule
3. Deep-dive into weak areas → Use the section guides for targeted practice
4. Practice with curated lists → Work through the Core 50 problems
5. Prep for specific companies → Read the Company Guides