About the role
About The Role Join Rex.zone to produce high-quality training data for machine learning and LLM systems. You will label and evaluate text and multimodal content, follow strict annotation guidelines, and support RLHF and QA workflows that improve model performance and content safety in real-world AI/ML pipelines.
Key Responsibilities
- Execute text and prompt evaluation tasks, including LLM response grading
- Perform RLHF preference ranking and write clear rationales
- Conduct QA evaluation checks and dataset audits to improve training data quality
- Apply named entity recognition (NER) and taxonomy-driven labeling
- Support computer vision annotation (e.g., bounding boxes, polygons, segmentation) when required
- Perform content safety labeling and policy-based classification
- Participate in calibration sessions to reduce inter-annotator disagreement
Requirements
- Hands-on experience with data annotation or evaluation for ML/LLM systems
- Strong attention to detail and consistent decision-making
- Comfort working with ambiguity and documenting edge cases
- Familiarity with RLHF, LLM evaluation, content safety labeling, and QA concepts
- Ability to work remotely with reliable connectivity and maintain confidentiality
Preferred Qualifications
- Experience with annotation tools, audits, and disagreement resolution workflows
- Exposure to computer vision annotation and spreadsheet-based QA
- Bilingual English/French helpful for Canada-focused datasets
Compensation Competitive hourly rate: $30–$50/hr.
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