Lessons learned when hiring engineers in the age of AI

AI-assisted app-building exercises are increasingly useful only as an initial screen because modern coding models can produce working software without revealing whether a candidate can independently own architecture, make sound trade-offs, or recognize flawed agent decisions. The author argues that effective AI challenges quickly become outdated as models improve, while restrictive workplace AI budgets can limit candidates’ experience with frontier tools and should be distinguished from lack of ability. Live observation of a candidate using AI, including their model choices, delegation habits, pragmatism, and willingness to intervene for specific risks, is presented as more informative than self-reported workflows, while take-home assignments are criticized for favoring candidates with more free time or greater need for a job. As AI-enabled “vibe coding” broadens the applicant pool, the proposed hiring process combines an AI workflow screen with traditional no-AI interviews that assess unfamiliar problem-solving, system-design judgment under changing constraints, and an additional “spark” of exceptional potential demonstrated through impact, progression, persistence, passion, or broader accomplishments. The central aim is to hire engineers who can use AI effectively while retaining responsibility for the systems and decisions it helps create.

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