This free AI Evaluation Engineer job description template is ready to use — copy it, replace the {{placeholders}}, and post your role in minutes. It includes a company intro, a role summary, responsibilities, requirements, nice-to-haves, and compensation, with writing tips and FAQs below to help you tailor it to your team.
When to use this template
Use this when you're hiring someone to own evaluation — building the benchmarks, test sets, and measurement that tell you whether your AI features actually work and whether changes make them better or worse. It's an increasingly critical role as teams ship more AI.
Evaluation candidates want to know how mature your eval practice is, what you're measuring, and how the role connects to AI engineers, prompt engineers, and product. Be specific.
If the role is mainly building features, use the AI Engineer template; if it's prompt design, use the Prompt Engineer template.
Writing tips
- Describe what you need to measure and how mature your eval practice is.
- Emphasize rigor — good evals are the difference between shipping and guessing.
- Clarify how the role connects to AI engineering, prompt engineering, and product.
- Mention any human-in-the-loop or labeling work involved.
- Include the salary range and seniority level.
The job description
Copy the template below and replace the {{placeholders}} and [bracketed notes] with your specifics.
About {{company}}
{{company}} is [what you do]. As we ship more AI, we're hiring an AI Evaluation Engineer to tell us — rigorously — whether it's any good and whether our changes help.
The role
As an AI Evaluation Engineer, you'll build the evals, benchmarks, and measurement that quantify the quality of our AI features. You'll turn fuzzy notions of 'good' into metrics, catch regressions, and give the team the confidence to ship. This role reports to {{hiring_manager}} and is based {{work_type}} in {{location}}.
What you'll do
- Build evaluation sets, benchmarks, and metrics for our AI features.
- Turn subjective quality goals into measurable signals.
- Catch regressions and quantify the impact of prompt and model changes.
- Set up human-in-the-loop review and labeling where needed.
- Partner with AI and prompt engineers to close the loop on quality.
What we're looking for
- 3+ years in ML, data, or AI engineering with an evaluation focus.
- A rigorous, experimental mindset and strong statistical sense.
- Proficiency in [Python] and comfort building eval tooling.
- An understanding of how LLMs and AI systems fail.
- Clear communication of what the numbers actually mean.
Nice to have
- Experience with LLM eval frameworks and benchmarks.
- Background in data labeling or human-in-the-loop systems.
- A research or applied science background.
What we offer
- Salary range: {{salary_range}}, plus equity.
- [Comprehensive benefits].
- Flexible {{work_type}} working and [PTO policy].
- Ownership of the signal that tells us whether our AI is working.
How to personalize
Replace these placeholders before posting:
- {{company}}
- {{location}}
- {{work_type}}
- {{salary_range}}
- {{hiring_manager}}
The bracketed notes — like [your benefits] or [your primary language(s)] — are prompts to swap in your own details. The more specific you are about the actual work and stack, the stronger your applicant pool will be.
Frequently asked questions
- What does an AI Evaluation Engineer do?
- An AI Evaluation Engineer builds the evaluations, benchmarks, and metrics that measure how well AI features work. They turn subjective quality goals into measurable signals, catch regressions, quantify the impact of changes, and give teams the confidence to ship AI reliably.
- Why do AI teams need evaluation engineers?
- Because AI outputs are non-deterministic and hard to judge, teams that ship without rigorous evaluation are essentially guessing. Evaluation engineers make quality measurable — turning 'it seems better' into evidence, catching regressions, and enabling safe, fast iteration on prompts and models.
- What skills should an AI Evaluation Engineer have?
- A rigorous, experimental mindset, a strong statistical sense, proficiency in Python and eval tooling, and an understanding of how LLMs and AI systems fail. The ability to clearly communicate what the numbers mean is just as important as building the evals.