Be prepared for live prototyping rounds in senior AI PM interviews. Interviewers provide an IDE and expect you to build an idea. They evaluate your problem-solving process, how you collaborate with the AI, and your ability to navigate trade-offs, not just raw coding skill.

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.

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.

Ditch traditional coding interviews. To hire for the AI era, ask candidates to record a full-screen video of themselves using AI agents to build a feature. This reveals their true skill: how they prompt, manage, and debug AI, which is more valuable than their ability to write code from scratch.

Sierra transformed its hiring by replacing traditional coding challenges with real-world tasks. Candidates get a prompt and a $150 token budget to build an application using their preferred AI coding agents. This tests modern, AI-native problem-solving skills, not rote memorization or algorithm theory.

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.

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.