TL;DR: We’re two experienced engineers and founders compiling practical approaches to hiring AI engineers, identifying AI-native talent, and improving hiring with AI. Breakfast or lunch is on us, and we’ll share the full anonymized report with everyone we meet.
We’re Greg and Corentin, two engineers turned founders. Greg was an early AI tech lead at Stripe. Corentin co-founded GitStart (YC S19), which vetted and hired 400+ engineers through its platform.
We’re spending Aug 19–22 in NYC and Aug 23–Sep 8 in SF on a field trip to meet founders, engineering leaders, and heads of talent who are actively hiring AI engineers or strongly selecting for AI skills in other roles.
We’ll treat you to breakfast or lunch to talk about what you’ve been trying and how your approach compares with what others are doing. We’ll compile everything we hear into a short, anonymized report and share it with everyone we meet within 3 weeks of the trip.
We’re especially interested in:
- How are you evaluating AI engineers? How are you defining the scorecard when it’s changing so quickly? How are you evaluating candidates when core skills like building evals are hard to assess in a traditional interview?
- How are you evaluating AI-native talent more broadly? How do you hire for emerging versions of existing roles? For example, if you hire a growth marketer, do you simply ask, “Show me your agent setup,” and look at past results? Do you run work trials? Or do you look for signals that someone who isn’t AI-native yet can quickly become so?
- How are you handling the flood of AI-generated applications? We hear the numbers game got automated with AI on the candidate side, and human screening is breaking. Do you fight fire with fire, applying AI to weed out AI noise, and do you avoid missing out hidden gems?
- How else are you improving the hiring process with AI? Some teams are seeing gains in sourcing efficiency from databases like Exa and connecting their ATS to agent harnesses that review candidates automatically. What have you tried?
- Have you tried rebuilding the interview process with AI? What would you change if Listen Labs–style AI interviews made reference checks cheap and effortless enough to run much earlier?
We’ll also bring a few fun hypotheticals: what would you do with an unlimited supply of 7-out-of-10 recruiters at your disposal?
We’re also deeply interested in negative results: what did you try with AI in hiring that didn’t work?