Chemical Engineering AI Evaluator

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

About the role

Chemical Engineering AI Evaluator is a remote evaluation track for reviewing chemical engineering ai evaluation prompts and responses against AuraOne's quality rubric. Reviewers compare paired outputs, label edge cases, and write the kind of structured feedback the modeling team can use to retrain.
AI data reviewers help turn chemical engineering ai evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Review frontier model outputs. Judge benchmark failures and calibrate other evaluators.

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.

Role details

Track Evaluation & annotation Work model Remote · Independent specialist contractor Compensation Hourly rate confirmed after the interview process. Eligible from US

What you should bring

• 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.

Role signals

Example tasks

• Compare two chemical engineering ai evaluation model responses to the same prompt and pick the stronger one with rationale.
• Tag an unsafe response with the correct policy category and severity.
• Audit a 50-row batch for rubric consistency and report drift to the program lead.
• Propose a rubric clarification after spotting a recurring failure mode.

Useful experience

• Background in linguistics, content moderation, or trust & safety review.
• Experience with inter-rater agreement metrics and calibration cycles.
• Domain expertise that lets you spot subject-matter errors automated checks miss.

Compensation and schedule

Hourly rate confirmed after the interview process.
Expected arrangement: contractor , with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.

Skills used in matching

• 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

Application boundary

Creating a specialist profile records your experience and preferences. Starting role intake is a separate action that attaches this role to your candidate record.

Specialist intake

The intake preserves your chosen role, the visible terms, and source attribution for reviewer context.

• 01 Confirm profile and eligibility details.
• 02 Attach this role deliberately.
• 03 Receive a human review decision or follow-up.
  Placement timing depends on program demand and reviewer confirmation.