The #1 Hiring Mistake AI Startups Make When Scaling Their Engineering Team

AI hiring is broken.

I just saw another startup burn 3 months looking for a “10x AI engineer” only to lose their top candidate to a FAANG offer.

Why? Because they’re hiring like a SaaS company instead of an AI lab.

The Fatal Flaws in AI Hiring:

❌ Overvaluing degrees, undervaluing real-world problem-solving. A PhD from Stanford is great. But can they ship real models?

❌ Long, broken hiring processes. If you’re taking 4 weeks to make a decision, you’ve already lost.

❌ Job descriptions that read like academic papers. AI engineers don’t want buzzwords—they want high-impact, real-world problems.

How to Actually Win AI Talent:

✅ Hire for problem-solving, not just credentials. Kaggle grandmasters, open-source contributors, and ex-researchers with shipped models > fancy degrees.

✅ Move fast. The best AI engineers are gone in 10 days. Your hiring process better be faster than Google’s.

✅ Sell the mission. AI engineers don’t join startups for the money (FAANG pays more). They join to solve frontier problems with impact.

💡 Hiring AI talent? We place top engineers in high-growth startups—fast. DM me.