# alexeygrigorev/ai-engineering-field-guide

Research into AI engineering interview assignments, take-home challenges, and hiring practices from 2026

Repository: https://github.com/alexeygrigorev/ai-engineering-field-guide
Canonical: https://ross.abutalabs.com/products/ai-engineering-field-guide
Language: HTML
License Family: other
Last push: 2026-08-25T15:55:37+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 35, longevity 15
- inputs: {"age_days": 210, "days_push": 8, "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 5389, forks 533 (observed 2026-08-28T04:09:17.170404+00:00)

## What it is
A data-driven field guide to AI engineering roles, skills, and interviews, built from analysis of thousands of real job descriptions, interview experiences, and practitioner stories. It covers the AI engineer role, interview preparation materials, and learning paths for career transitions.

## Use cases
- prepare for an AI engineering interview
- find take-home assignment examples for AI engineer roles
- understand what skills AI engineering job postings require
- plan a learning path to become an AI engineer
- research AI engineer salary and offer negotiation
- compare interview processes across companies
- transition from data engineering to AI engineering

## When to choose
- you are preparing for AI/LLM engineering interviews and want real, data-backed question banks
- you want to understand the AI engineering job market from actual job postings
- you need curated take-home assignment examples and portfolio guidance

## When to avoid
- you need a software tool or library rather than written guidance
- you want formal ML theory coursework or academic material
- you need guaranteed up-to-date content, since insights reflect scraped data snapshots

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: artificial-intelligence, large-language-models, tutorials, education, hr
- platform: -
- tags: ai-engineering, interview-preparation, career-guide, job-market-analysis, field-guide, hiring, web-server

## Member repositories
- alexeygrigorev/ai-engineering-field-guide (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:17.170404+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 2026-08-29T17:58:24.185657+00:00, confidence not recorded.
  - readme: https://github.com/alexeygrigorev/ai-engineering-field-guide (fetched 2026-08-28T04:09:17.170404+00:00, sha 9c86c54eaaa1)
- Data as of 2026-08-30T08:39:29.467469+00:00.