To familiarize engineers with agentic coding workflows, Brex created a new interview process that requires AI tool usage. They then had every current engineer and manager complete the interview, forcing hands-on experience and revealing skill gaps in a practical setting.

To find talent capable of managing an AI stack, traditional interviews are insufficient. A better test is to provide candidates with platform credits (e.g., Replit) and challenge them to build a functional agent that automates a real business task, proving their practical skills.

Theoretical knowledge is now just a prerequisite, not the key to getting hired in AI. Companies demand candidates who can demonstrate practical, day-one skills in building, deploying, and maintaining real, scalable AI systems. The ability to build is the new currency.

The VP of Search stated that technical interviews must now assess a candidate's ability to use AI coding assistants effectively. The goal is to measure not only problem-solving skills but also fluency with new tools that change how the job is performed, going beyond simply asking un-googleable questions.

Dreamer's hiring process now evaluates an engineer's ability to work with and through AI coding agents. Beyond a basic coding screen, the main interview involves a project built using tools like Codex, testing the candidate's skill in prompting, reviewing, and orchestrating AI to be productive.

Traditional product sense questions are being replaced. AI PM candidates should expect to solve problems live using AI tools or design complex AI-native systems. This shift assesses a candidate's hands-on "builder" capabilities and deep understanding of modern AI architecture.

To build an AI-native team, shift the hiring process from reviewing resumes to evaluating portfolios of work. Ask candidates to demonstrate what they've built with AI, their favorite prompt techniques, and apps they wish they could create. This reveals practical skill over credentialism.

To hire for the AI era, HubSpot fundamentally changed its marketing hiring process for every role. Instead of asking candidates to create strategy decks, they now require applicants to build solutions with AI during the interview, testing practical application and AI fluency over theoretical knowledge.

Since AI assistants make it easy for candidates to complete take-home coding exercises, simply evaluating the final product is no longer an effective screening method. The new best practice is to require candidates to build with AI and then explain their thought process, revealing their true engineering and problem-solving skills.