Technical Paper and Evidence Reviewer

Technical Paper and Evidence Reviewer is a remote evaluation track for reviewing technical paper and evidence evaluation prompts and responses against Aura One'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 technical paper and evidence evaluation outputs into auditable labels, rationales, and regression cases for Aura One Human Data.

Review frontier model outputs. Judge benchmark failures and calibrate other evaluators.

• Evaluate technical paper and evidence evaluation model outputs against a versioned rubric and assign severity tags for Technical Paper and Evidence Reviewer 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.

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 technical paper and evidence evaluation or adjacent content for Technical Paper and Evidence Reviewer 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 technical paper and evidence 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
• Technical Paper and Evidence evaluation
• Science and advanced mathematics
• AI evaluation
• Rubric writing
• Expert review