What it does

This repo is a living research project that catalogs what AI engineering actually looks like in practice. Alexey Grigorev scraped 2,445 real job descriptions from builtin.com, collected interview experiences from 51 companies, and synthesized learning paths for people transitioning from data science, ML engineering, backend, and frontend roles. The result is part career guide, part hiring intelligence, part confession that the field is still being invented.

The interesting bit

The raw data is right there in the repo — structured YAML files of job postings, raw extracts, and company-by-company interview breakdowns. Most career advice is opinion dressed as fact; this at least shows its homework, even if the analysis is still being written.

Key highlights

• 5,694+ job responsibilities analyzed for patterns in what companies actually want
• 4,525 real use cases showing what gets built, not what gets pitched
• Interview questions consolidated from 100+ sources, including take-home assignments scraped from 100+ GitHub repos
• Role-specific transition guides with rough time estimates (backend engineers: 2–3 months; data engineers: 3–4 months)
• 4-part webinar series with recordings available for the first two sessions

Caveats

• “Coming soon” sections for salary data and community contributions — not there yet
• Job market data is limited to builtin.com postings from six cities; no claim about representativeness
• The author runs a paid course and newsletter; the repo is clearly part of that ecosystem

Verdict

Worth bookmarking if you’re hiring or job-hunting in AI engineering and tired of vaporware advice. Skip it if you want finished analysis rather than a work-in-progress data dump with commentary.