Agentic Task Completion Failure Analysis Reviewer is a remote evaluation track for reviewing agentic task completion failure analysis 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.
Why this role matters
AI data reviewers help turn agentic task completion failure analysis evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Responsibilities
- Evaluate agentic task completion failure analysis evaluation model outputs against a versioned rubric and assign severity tags for Agentic Task Completion Failure Analysis 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.
- Maintain reviewer-quality scores by calibrating against gold-standard examples each week.
- Document recurring failure modes so the modeling team can target them in the next training run.
Qualifications
- Prior evaluation, annotation, or human-rater experience on agentic task completion failure analysis evaluation or adjacent content for Agentic Task Completion Failure Analysis 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.
Example tasks
- Compare two agentic task completion failure analysis 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.
Nice to have
- 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.
Skills
- Model output evaluation
- Rubric-based annotation
- Severity tagging
- Inter-rater calibration
- Agentic Task Completion Failure Analysis evaluation
- Frontier evaluation
- Rubric calibration
- Failure analysis
- Agentic
- Task
- Completion
Work model
Remote --- US-eligible. Remote · Independent specialist contractor. Employment type: CONTRACTOR. Applicants must be authorized to work from US.
Compensation
Hourly rate confirmed after the interview process.
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Agentic Task Completion Failure Analysis Reviewer Arbeitgeber: AuraOne Human Data
AuraOne ist ein hervorragender Arbeitgeber, der seinen Mitarbeitern die Möglichkeit bietet, in einem dynamischen und unterstützenden Umfeld zu arbeiten. Mit einem Fokus auf kontinuierliches Lernen und Entwicklung fördert das Unternehmen eine Kultur der Zusammenarbeit und des Austauschs, während es gleichzeitig flexible Arbeitsbedingungen für Remote-Mitarbeiter bietet. Die Rolle des Machine Learning Expert ermöglicht es den Mitarbeitern, direkt zur Verbesserung von KI-gestützten Prozessen beizutragen und dabei wertvolle Erfahrungen in der Maschinenlernoperation zu sammeln.
