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LLM Evaluator

An LLM Evaluator measures how well large language models and AI features perform. They design test sets, rubrics, and scoring methods, run human and automated evaluations, and surface failure patterns like hallucination or bias. The role gives teams the objective signal they need to improve prompts, models, and safety before and after release.

Responsibilities

Design evaluation sets and scoring rubrics
Run human and automated evaluations of model output
Identify hallucination, bias, and safety failures
Track quality metrics across model and prompt versions
Recommend improvements based on evaluation results
Build and maintain evaluation tooling and datasets

Must-have skills

Rigorous, detail-oriented analytical mindset
Understanding of LLM behavior and failure modes
Ability to design rubrics and measure quality
Basic scripting for automated evaluation
Clear reporting of findings

Nice-to-have skills

Statistics and inter-rater reliability knowledge
Experience with eval frameworks
Domain expertise for specialized evaluation
Prompt engineering familiarity

Average Salary

Typical US base salary for an HR Generalist by experience level.

Junior

0–2 yrs experience

$80,000 – $105,000
US annual salary (USD)

Intermediate

0–2 yrs experience

$105,000 – $140,000

US annual salary (USD)

Senior

0–2 yrs experience

$140,000 – $185,000

US annual salary (USD)

Figures are annual US market estimates for orientation, not offers. Actual pay varies by location, company stage, and equity.

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Frequently asked questions

What is LLM evaluation?
It is the practice of systematically measuring how well a language model performs on accuracy, safety, format, and tone, using test sets and scoring methods.
Why is evaluating LLMs hard?
Outputs are open-ended and context-dependent, so there is often no single correct answer. Good evaluation combines automated checks with careful human judgment.
Do LLM Evaluators need to code?
Light scripting helps automate scoring, but strong analytical judgment and understanding of model behavior matter most.
How does this role support AI safety?
By catching harmful, biased, or wrong outputs before and after release, evaluators give teams the evidence needed to make AI features safer and more reliable.

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