Hiring an AI Evals Engineer: In-House, Marketplace, or Specialist?
Your LLM feature needs real evaluation and someone has to build it. Here is an honest comparison of the three ways teams get it done — including when I'm not the right answer.
Full-time evals engineer
Right when evaluation is a daily need
- Deep, permanent context on your product
- Months to hire — evals is a scarce specialty
- Six-figure commitment before the first eval exists
- Best once you have several AI features in production
Generalist freelancer / marketplace
Cheap to start, risky on depth
- Fast to engage, hourly pricing
- Evaluation is usually a sideline, not a specialty
- Open-ended scope — you manage the project
- Workable for small, well-defined eval tasks
Specialist consultant (fixed-scope sprint)
Fastest route to a working eval system
- Working evaluation harness in 10–14 days
- Fixed price and deliverables agreed up front
- Your team keeps and runs everything afterwards
- Not a substitute for in-house once scale demands it
The common failure is doing nothing while deciding: shipping AI features for months with no evaluation at all. Whatever you choose, get a baseline first — my evaluation framework guide shows what a minimum viable eval looks like, and the consultant route exists when you want it built for you.
Frequently asked questions
What skills should an AI evals engineer have?
Four things: (1) failure-mode thinking — the instinct to find where models break, not confirm they work; (2) dataset and rubric design — building representative test sets and unambiguous scoring criteria; (3) LLM-as-a-Judge literacy — knowing judge biases and how to calibrate against human labels; (4) engineering — wiring evals into CI so quality is checked on every change, not once.
How much does it cost to hire for AI evals?
Full-time evaluation engineers command senior-ML salaries, and hiring typically takes months. Marketplace freelancers bill hourly with open-ended scope. A fixed-scope evaluation sprint runs from $7,500 and delivers a working eval system in 10–14 days — a common starting point before committing to a hire.
When should we hire in-house instead of using a consultant?
When evaluation becomes daily work: multiple AI features in production, frequent model swaps, a growing eval suite that needs an owner. A common path is consultant-first — get the harness, rubrics, and baselines built — then hire in-house to own and extend a system that already works.
Can our regular engineers just write the evals?
They can — and should — maintain them. But teams writing their first eval suite usually test the happy path they already believe in. The value of a specialist is adversarial coverage: the tool-use edge cases, ambiguous inputs, and failure modes that only show up after you have broken a lot of models on purpose.