Remote Chemical Engineering AI Evaluator & Labeler

AI Trainer Jobs; United States; Remote; USD 41,000 - 69,000; Part time

Job summary

AI Trainer Jobs is seeking remote contractors to evaluate chemical engineering AI outputs against a versioned rubric. You will compare paired responses, write rationales, and label issues with structured tags to help retrain the model.

Ideal candidates have prior evaluation experience, can apply rubrics across large batches, and provide clear, concise reasoning. A reliable weekly async availability of at least 10 hours is expected for ongoing review work.

Qualifications

• Prior evaluation, annotation, or human-rater experience on chemical engineering AI evaluation or adjacent content for Chemical Engineering AI Evaluator work.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and the rubric clause being applied.
• Strong attention to detail and the ability to flag when a prompt itself is the problem.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate chemical engineering AI evaluation model outputs against a versioned rubric and assign severity tags for Chemical Engineering AI Evaluator assignments.
• Compare paired responses and pick the stronger answer with a written rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route them back to the program team for rubric updates.
• Maintain reviewer-quality scores by calibrating against gold-standard examples each week.

Skills

Model output evaluation

Rubric-based annotation

Severity tagging

Inter-rater calibration

Chemical Engineering AI evaluation

Science and advanced mathematics

AI evaluation

Rubric writing

Expert review

Job description

AI Trainer Jobs is seeking remote contractors to evaluate chemical engineering AI outputs against a versioned rubric. You will compare paired responses, write rationales, and label issues with structured tags to help retrain the model.

Ideal candidates have prior evaluation experience, can apply rubrics across large batches, and provide clear, concise reasoning. A reliable weekly async availability of at least 10 hours is expected for ongoing review work.