AI Hiring Checklist for Engineering Managers

This checklist helps engineering managers and CTOs at U.S. companies decide where AI belongs in software engineering hiring and where it doesn't. Use it before adopting an AI recruiting or scheduling tool, before restructuring a technical interview loop, or before vetting a nearshore partner's AI practices. It walks through diagnosing your actual bottleneck, automating screening and scheduling without losing judgment on final decisions, keeping a human owner on every hire, and checking any AI hiring tool for bias, data retention, and compliance risk before you rely on it. The last section applies the same standards to nearshore and distributed hiring, where vetting through a partner raises the stakes. Read it once before adoption, then revisit it each quarter.

What you get

• Diagnose whether your bottleneck is sourcing, vetting, or retention before adopting any AI hiring tool.
• Use AI to automate resume screening and interview scheduling, but cap how much weight its ranking carries in final decisions.
• Keep a named human owner on every technical interview and final hiring decision, especially when candidates use AI coding assistants.
• Vet any AI hiring tool for training data bias, data retention practices, and compliance with state hiring laws before you rely on it.
• Apply the same interview rigor and AI governance checks to nearshore and distributed hiring, where vetting through a partner raises the stakes.