# khangich/machine-learning-interview

Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

Repository: https://github.com/khangich/machine-learning-interview
Canonical: https://ross.abutalabs.com/products/machine-learning-interview
License Family: other
Topics: interview-preparation, deep-learning, system-design, mvp, interivew, machine-learning, leetcode, interview-questions
Last push: 2023-08-31T17:49:30+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2213, "days_push": 1098, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 12795, forks 2045 (observed 2026-08-28T04:10:59.827419+00:00)

## What it is
A curated study guide and knowledge base for machine learning interviews at top tech companies, covering ML system design, deep learning concepts, and coding questions. It accompanies the author's books and courses, drawing on interview experience from Google, LinkedIn, Snap, and other companies.

## Use cases
- prepare for a machine learning interview at FAANG
- study ML system design questions like YouTube recommendations or feed ranking
- find a minimum viable study plan for ML interviews
- practice leetcode-style questions for ML roles
- learn how recommendation and ranking systems are designed
- review common machine learning interview questions

## When to choose
- you are interviewing for ML engineer or data scientist roles at large tech companies
- you want structured, experience-based guidance on ML system design interviews
- you need a free, community-vetted study plan with real interview examples

## When to avoid
- you need a software library or tool rather than study material
- you are looking for general software engineering interview prep without an ML focus
- you need formally licensed or maintained educational content for commercial training use

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, developer-tools
- domain: machine-learning, education, tutorials, artificial-intelligence
- platform: cross-platform
- tags: interview-preparation, leetcode, system-design, faang, study-guide, career

## Member repositories
- khangich/machine-learning-interview (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:59.827419+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 2026-08-29T17:13:47.170389+00:00, confidence not recorded.
  - readme: https://github.com/khangich/machine-learning-interview (fetched 2026-08-28T04:10:59.827419+00:00, sha bec5b9f460fe)
- Data as of 2026-08-30T08:39:29.467469+00:00.