RRepoGEO

REPOGEO REPORT · LITE

alirezadir/Machine-Learning-Interviews

Default branch main · commit 164d43a8 · scanned 5/11/2026, 3:27:34 AM

GitHub: 8,158 stars · 1,461 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface alirezadir/Machine-Learning-Interviews, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the 'News' section in the README

    Why:

    CURRENT
    The 'News' section appears directly after the H1.
    COPY-PASTE FIX
    Move the 'News' section to appear *after* the main introductory paragraph that describes this repository's purpose (i.e., after 'This repo aims to serve as a guide to prepare for Machine Learning (AI) Engineering interviews...').
  • highhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://alirezadir.com/ml-interviews (or similar relevant URL if a dedicated page exists)
  • mediumreadme#3
    Strengthen the README's opening to highlight unique value proposition

    Why:

    CURRENT
    This repo aims to serve as a guide to prepare for **Machine Learning (AI) Engineering** interviews for relevant roles at big tech companies (in particular FAANG). It has compiled based on the author's personal experience and notes from his own interview preparation, when he received offers from Meta (ML Specialist), Google (ML Engineer), Amazon (Applied Scientist), Apple (Applied Scientist), and Rok
    COPY-PASTE FIX
    This repository is the definitive, experience-driven guide for **Machine Learning (AI) Engineering** technical interviews, specifically designed to help candidates secure roles at top-tier tech companies like FAANG. Unlike general ML learning resources, this guide focuses exclusively on the practical, frequently asked questions and system design challenges encountered in real-world interviews, compiled from the author's successful interview preparation leading to offers from Meta, Google, Amazon, and Apple.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface alirezadir/Machine-Learning-Interviews
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Machine Learning System Design Interview
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Machine Learning System Design Interview · recommended 2×
  2. Machine Learning Yearning · recommended 1×
  3. An Introduction to Statistical Learning with Applications in R · recommended 1×
  4. The Elements of Statistical Learning · recommended 1×
  5. Coursera: Machine Learning by Andrew Ng · recommended 1×
  • CATEGORY QUERY
    Seeking a comprehensive guide to prepare for machine learning and AI engineering technical interviews.
    you: not recommended
    AI recommended (in order):
    1. Machine Learning Yearning
    2. An Introduction to Statistical Learning with Applications in R
    3. The Elements of Statistical Learning
    4. Coursera: Machine Learning by Andrew Ng
    5. Deep Learning Specialization
    6. Machine Learning Engineering for Production (MLOps) Specialization
    7. PyTorch
    8. TensorFlow/Keras
    9. scikit-learn
    10. LeetCode
    11. Cracking the Coding Interview by Gayle Laakmann McDowell
    12. Designing Data-Intensive Applications by Martin Kleppmann
    13. Machine Learning System Design Interview
    14. Grokking the System Design Interview
    15. Probability and Statistics for Engineers and Scientists by Walpole, Myers, et al.
    16. Khan Academy: Statistics and Probability
    17. STAR Method
    18. Cracking the PM Interview by Gayle Laakmann McDowell and Jackie Bavaro
    19. Pramp.com
    20. Interviewing.io
    21. arXiv.org
    22. Papers With Code
    23. Twitter
    24. Google AI Blog
    25. OpenAI Blog
    26. Meta AI Blog

    AI recommended 26 alternatives but never named alirezadir/Machine-Learning-Interviews. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find resources for AI agentic systems and scalable AI engineering interview preparation?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Designing Data-Intensive Applications
    4. Hugging Face Transformers
    5. Accelerate
    6. Machine Learning System Design Interview
    7. ByteByteGo
    8. OpenAI API
    9. Assistants API

    AI recommended 9 alternatives but never named alirezadir/Machine-Learning-Interviews. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of alirezadir/Machine-Learning-Interviews?
    pass
    AI did not name alirezadir/Machine-Learning-Interviews — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts alirezadir/Machine-Learning-Interviews in production, what risks or prerequisites should they evaluate first?
    pass
    AI named alirezadir/Machine-Learning-Interviews explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo alirezadir/Machine-Learning-Interviews solve, and who is the primary audience?
    pass
    AI did not name alirezadir/Machine-Learning-Interviews — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of alirezadir/Machine-Learning-Interviews. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/alirezadir/Machine-Learning-Interviews.svg)](https://repogeo.com/en/r/alirezadir/Machine-Learning-Interviews)
HTML
<a href="https://repogeo.com/en/r/alirezadir/Machine-Learning-Interviews"><img src="https://repogeo.com/badge/alirezadir/Machine-Learning-Interviews.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

alirezadir/Machine-Learning-Interviews — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite