alirezadir/AIMLInterviews resource

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9474 stars · 1668 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A curated study guide for AI and machine learning technical interviews at big tech companies, covering DSA coding, ML coding, ML/LLM fundamentals, GenAI and agentic system design, and behavioral interviews. It is organized as Jupyter Notebook-based chapters compiled from the author's own successful FAANG interview experiences.

Use cases

• prepare for machine learning engineer interviews at FAANG
• study ML system design questions for tech interviews
• practice ML coding interview problems
• review LLM and GenAI interview fundamentals
• prepare for agentic AI system design interviews
• get ready for behavioral and leadership interview rounds
• find GenAI learning resources for interview prep

When to choose

• you are interviewing for ML engineer, applied scientist, or AI engineer roles at large tech companies
• you want a structured, experience-based roadmap covering coding, fundamentals, system design, and behavioral rounds
• you need coverage of modern topics like LLMs, GenAI, and agentic AI systems in interview format

When to avoid

• you need a formal course with graded exercises or certification rather than a self-study guide
• you are looking for entry-level or non-ML software engineering interview prep
• you want interactive practice with automated feedback rather than reading material

Facets

learning-resource · maturity active

machine-learning deep-learning llm-inference agent-framework rag developer-tools machine-learning artificial-intelligence large-language-models education tutorials python cross-platform interview-preparation faang ml-system-design behavioral-interviews study-guide jupyter-notebooks ai-agents

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