AI-Era Engineering Roles

The roles AI-native companies are hiring for that did not exist three years ago. What each one is, what they cost, and how to hire them.

Forward deployed engineering

The fastest-growing engineering title in startups now has its own cluster: what the role is, how it differs from solutions engineering, how to interview for it, and when to hire.

What is a GTM engineer?

A technical operator who builds the systems behind go-to-market rather than running plays inside a platform. Why demand shifted from sales enablement tooling to this role.

What is an applied AI engineer?

Builds products on top of models that already exist: retrieval, evaluation, guardrails, cost and latency. Why most companies hiring for this write an ML engineer job description by mistake.

What is a founding engineer and when should you hire one?

Equity, scope and decision rights closer to a founder than an employee. Why the window for this hire is narrower than founders expect.

What do AI-era engineering roles pay?

Benchmarks for forward deployed, GTM and applied AI engineers, and why pricing them against the function they sit in loses candidates at offer stage.

How do you hire for a role you have never hired before?

Define the outcome, evaluate the reasoning, price against the outside option. What to do when there is no pattern to match against.

Does your startup need a Head of AI?

Usually not, and the reasons are structural. When the role earns its seat, what to build instead below 50 people, and the behavior screen if you do hire one.

How do I hire a GTM engineer?

Scoping the role, sourcing for behaviour rather than the title, testing whether a candidate builds or configures, and vetting when nobody internally has done the job.