The First Ten AI Engineers at Your Startup
Who to hire — and in what order — for the first ten engineering seats at an AI-native startup. A practical sequencing guide for founders past their seed round.
Most AI startups blow their first ten engineering hires. They over-index on research talent, hire a platform engineer too late, and end up with a brilliant prototype no customer can actually use. The order of your first ten hires is more important than any individual on the list.
Hires 1–3: ship the wedge
Your first three engineers should be full-stack builders who can take a vague problem statement and ship something a customer pays for in under six weeks. At this stage you do not need an ML researcher. You need someone who has wired up a RAG pipeline, deployed it on a real cloud, and watched it fail in front of users.
Hires 4–6: harden the loop
Once the wedge has paying users, hire for the parts that are breaking. That usually means an evals-minded engineer (so you stop shipping regressions), a data/integration engineer (so onboarding a new customer takes hours, not weeks), and a forward deployed engineer who lives inside your top accounts.
Hires 7–10: the platform
By the time you are at ten engineers you should have a platform engineer, a second FDE for the next vertical, an agent/orchestration specialist if your product is agentic, and one true ML/research-leaning engineer to push frontier capabilities. Not before.
Common mistakes
- Hiring a research lead before the product has any users.
- Hiring three platform engineers and zero people who talk to customers.
- Splitting agent work and RAG work across two teams that don't talk.
- Treating prompt engineers as a separate, lower tier of hire.
The pattern that works: hire generalists who have shipped AI features in production, layer in specialists only once the generalists hit a wall, and keep at least one engineer per five embedded with customers.