alexeygrigorev/ai-engineering-field-guide

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

What this is

A free, data-backed guide to becoming and working as an AI engineer. Instead of opinions or AI-generated filler, everything in it is drawn from real evidence: 6,964 actual job descriptions, real interview experiences, and practitioner stories.

What's inside

1. The AI Engineer Role — What the job actually looks like, based on patterns extracted from thousands of job postings: top skills, responsibilities, real-world use cases (4,525 of them), how the advertised role compares to reality, and market trends tracked month over month.

2. Interview Preparation — A deep breakdown of the hiring process: common interview formats, consolidated questions from 100+ sources (theory, coding, project deep-dives, system design, behavioral, take-home assignments), what skills actually get people hired, how to handle offers and rejections, and company-by-company data for 51 companies.

3. Learning Paths — What to learn and in what order, with specific transition guides for people coming from five backgrounds: Data Engineer (3–4 months), Data Scientist, ML Engineer (easiest transition), Backend Engineer (2–3 months), and Frontend Engineer.

4. Portfolio Guide — How to choose, build, and present portfolio projects.

5. Job Market Data — The raw and structured data behind the analysis: 6,964 job descriptions scraped from builtin.com across LA, NY, London, Amsterdam, Berlin, and India, organized by scrape date.

6. Awesome AI Engineering — A curated list of external resources compiled during research: practitioner interviews, system design guides, engineering blogs, books, courses, and case studies.

7. Webinars — Recorded sessions on the day-to-day of an AI engineer, how companies define the role, interview processes, take-home assignments, and portfolio project selection.

8. Learning Resources — A 9-week intensive course (RAG to agents) and a practitioner community called AI Shipping Labs.

Who it's for

Anyone exploring, transitioning into, or hiring for AI engineering roles — especially people coming from adjacent fields like data science, data engineering, ML engineering, or software engineering who want a realistic, evidence-based picture of the role.

Key takeaway

This is a research project, not a tutorial dump. Every claim about skills, trends, or interview patterns is grounded in scraped job data and real practitioner experiences — so readers get an accurate picture of the field rather than hype.

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