Hire AI evaluation engineers
Evaluation is the single highest-leverage hire in an AI team and the one most companies make last. Without it every prompt change is a coin flip, every incident is unreproducible, and nobody can answer whether last month's release made the product better.
Free to search, every filter open · opens filtered to AI Product Implementation.
35 scored profiles match — here are the top 6
How to tell a real one from a résumé that says the words
- Has built a labelled evaluation set from real traffic, not from imagination.
- Knows the failure modes of LLM-as-judge and uses it anyway, carefully.
- Ties evaluation to release: a regression suite that can actually block a deploy.
- Instruments production. Offline evaluation that never meets real traffic drifts quietly into fiction.
How to hire AI evaluation engineers with this database
- Filter by the AI Product Implementation capability, which is scored on guardrails, evaluation and monitoring rather than on shipping speed.
- Add the tooling you use — LangSmith, Braintrust, in-house harnesses.
- Ask how they'd measure quality for your product specifically. The good answer starts with sampling real usage, not with a metric name.
Every filter is free and each search shows the top 10 matches with the true total. The full ranked list is a single one-time payment — see pricing.
Questions about hiring AI evaluation engineers
What are AI evals?
Structured tests for non-deterministic systems: a set of representative inputs, a definition of a good output, and a scoring method — human, programmatic or model-graded — run repeatedly so quality changes are visible rather than felt.
Who should own evals — engineering, product or QA?
Whoever owns the definition of good. In practice it is usually a product-minded engineer, with the PM owning the criteria. What fails is when it belongs to nobody until something embarrassing ships.
Can we add evals later?
You can, and it costs more. Later means reconstructing what good looked like from memory and complaints instead of capturing it while the feature was being built.
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Everyone in here is scored 0–100 on what they have demonstrably shipped, with the reason behind every number.
Search the database →Scores reflect public professional signals only, and anyone can ask to be removed. Hiring decisions stay yours — this is a starting point, not a verdict on anyone's ability. Being hired for AI work yourself? Score your own profile.