Your First AI Engineer Should Not Be a Researcher
The biggest mistake AI startups make is hiring researchers to build products.
Why hiring an ML researcher as your first AI engineer usually fails. Learn what type of AI engineer actually drives early-stage success and how to identify them.
Who This Is For
This guide is essential for early-stage AI/ML startups making their first technical AI hire. It helps founders understand when they actually need an AI engineer versus when they need a strong generalist. Non-technical founders building AI products will learn what to look for. Key topics: Skills that matter for early-stage AI, how to evaluate AI engineering candidates, compensation ranges, and common hiring mistakes to avoid.
The Researcher Trap
Most early-stage AI startups overhire researchers when they need builders. The best first AI engineer is someone who can ship working products, not publish papers. They should have experience deploying ML models to production. They should understand infrastructure costs and trade-offs. They should be able to build a working MVP in weeks, not months. BeGlobal vets AI engineers for production experience, not just academic credentials.
Common questions