# alirezadir/AIMLInterviews

This repo is meant to serve as a guide for Machine Learning/AI technical interviews.

Repository: https://github.com/alirezadir/AIMLInterviews
Canonical: https://ross.abutalabs.com/products/aimlinterviews
Homepage: https://www.aimlinterviews.io/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: machine-learning, machine-learning-algorithms, ai, deep-learning, system-design, scalable-applications, interview, interview-preparation, interview-practice, interviews, agentic, ai-agents, ai-engineering
Last push: 2026-08-19T19:26:17+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 35, longevity 100
- inputs: {"age_days": 2040, "days_push": 14, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9474, forks 1668 (observed 2026-08-28T04:10:31.295887+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, llm-inference, agent-framework, rag, developer-tools
- domain: machine-learning, artificial-intelligence, large-language-models, education, tutorials
- platform: python, cross-platform
- tags: interview-preparation, faang, ml-system-design, behavioral-interviews, study-guide, jupyter-notebooks, ai-agents

## Member repositories
- alirezadir/AIMLInterviews (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:31.295887+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:22:04.550168+00:00, confidence not recorded.
  - readme: https://github.com/alirezadir/AIMLInterviews (fetched 2026-08-28T04:10:31.295887+00:00, sha af2dbab84cc4)
  - homepage: https://www.aimlinterviews.io/ (fetched 2026-08-29T08:22:01.209699+00:00, sha 2e92dcd8cbee)
- Data as of 2026-08-30T08:39:29.467469+00:00.