Rex.zone
Posted 177 days agoVerified live 1d ago
AI Data Labeling Jobs Brazil
Brief overview
Remote
$30–$50/hrStated range
Data annotationQA evaluationAnnotation guideline complianceAccuracy and consistency in labelingStructured labeling schemasRubric-based scoringRemote work independenceThroughput target managementRLHFPreference rankingHelpfulness and harmlessness evaluationsSpot checksConsensus reviewError categorizationPrompt evaluationInstruction tuningLLM evaluation
About the company
Rex.zoneremoexperts.com
RemoExperts (by Abaka AI) is a global platform where skilled professionals and tutors contribute to AI training across video, image, audio, text, code, math, and more.
Job description
Summary
Rex.zone is hiring for remote, full-time AI Data Labeling roles focused on training data creation and evaluation workflows for AI/ML systems. The role involves supporting LLM training pipelines and multimodal model development through consistent labeling and review.
Responsibilities
- Label text, image, and/or multimodal data following detailed annotation guidelines
- Perform RLHF tasks such as preference ranking and helpfulness/harmlessness evaluations
- Execute QA evaluation (spot checks, consensus review, error categorization) to improve training data quality
- Conduct prompt evaluation and rubric-based scoring for instruction tuning and LLM evaluation
- Document edge cases, propose clarifications, and contribute to feedback loops that improve model performance
- Support NLP labeling (intent, sentiment, toxicity, topic, summarization quality)
- Perform named entity recognition and entity linking
- Conduct content safety labeling for policy compliance and risk mitigation
- Perform computer vision annotation (bounding boxes, polygons, keypoints)
- Conduct grounding checks, reasoning quality ratings, and refusal/safety assessments
Skills
- Experience in data annotation or QA evaluation in production workflows
- Strong annotation guideline compliance, accuracy, and consistency
- Comfort with structured labeling schemas and rubric-based scoring
- Ability to work independently in a remote environment and meet throughput targets
- Exposure to RLHF, prompt evaluation, LLM evaluation, NER, content safety, or computer vision annotation
- Familiarity with inter-annotator agreement, sampling-based QA, audits, and error analysis
Qualifications
Must Haves
- Experience in data annotation or QA evaluation in production workflows
- Strong annotation guideline compliance, accuracy, and consistency
- Comfort with structured labeling schemas and rubric-based scoring
- Ability to work independently in a remote environment and meet throughput targets
Nice to Haves
- Exposure to RLHF, prompt evaluation, LLM evaluation, NER, content safety, or computer vision annotation
- Familiarity with inter-annotator agreement, sampling-based QA, audits, and error analysis