Traditional hiring practices fail to identify the engineers who will actually deliver in production AI systems. The difference between a successful AI product and a failed experiment often comes down to hiring decisions made months earlier.

The Challenge: Identifying Real AI Talent

The AI talent market presents unique challenges:

• Credential inflation (everyone claims ML experience)
• Theory vs. implementation gaps
• The difference between research and production mindsets
• Rapidly evolving skill requirements
• Competition from well-funded companies

Traditional interviews - whiteboard algorithms, system design discussions, behavioral questions - poorly predict success in AI engineering roles. We need a better approach.