AI Engineering Field Guide is a research resource that synthesizes data from job descriptions, interview experiences, and practitioner stories to document AI engineering roles, skills, and hiring practices.
The guide addresses the challenge of understanding what AI engineering actually entails and how to prepare for roles in the field. Rather than relying on speculation or AI-generated content, it grounds its insights in analysis of thousands of real job postings, interview processes, and use cases. The approach involves extracting patterns from actual hiring data, documenting common interview formats and questions, and mapping skill requirements across different company practices and role archetypes.
The resource suits developers considering a transition into AI engineering or those preparing for interviews in the field. It provides structured learning paths tailored to different backgrounds—data engineers, data scientists, ML engineers, backend engineers, and frontend engineers—each with estimated timelines for transition. The guide covers interview preparation across multiple dimensions: theory, coding, system design, behavioral questions, and take-home assignments. It also documents company-specific hiring processes for fifty-one organizations, allowing candidates to understand what to expect at particular employers. The material distinguishes itself by being grounded in real data rather than generic advice, making it particularly valuable for candidates seeking realistic assessments of what the role demands and how hiring actually works in practice.
The project maintains active documentation of job market trends through month-over-month analysis of role archetypes and skill stacks. Content spans multiple dimensions of the AI engineering landscape, from role vision and responsibilities to forward-deployed engineer specialization. The guide includes practical guidance on handling offers, rejections, and salary negotiation after interviews, alongside analysis of emerging trends like AI-proctored rounds and cheating detection in hiring processes.
