Remote AI Data Annotator: NLP & CV Labeling & Evaluation

Rex.zone; Paris; Sur place; EUR 35 893 - 59 822; Plein temps

Résumé du poste

Rex.zone connects you with remote data annotator roles focused on producing high-quality training data and evaluation signals for AI/ML systems. You will support LLM training pipelines and computer vision workflows through data labeling, RLHF preference ranking, prompt-response evaluation, and QA evaluation to improve model performance and reliability.

The work involves labeling for NLP tasks like text classification and named entity recognition, RLHF comparisons, rubric-based scoring, prompt

Qualifications

• Experience in data annotation/data labeling or related AI operations work.
• Strong attention to detail and ability to follow complex guidelines consistently.
• Comfort working remotely with clear written communication.
• Familiarity with rubrics, error taxonomies, and inter-annotator agreement concepts.

Responsabilités

• Create accurate labels for NLP tasks such as text classification and named entity recognition.
• Perform RLHF comparisons, rubric-based scoring, and write clear rationales.
• Complete prompt evaluation for helpfulness, instruction-following, and safety.
• Apply content safety labeling aligned to policy categories.
• Run QA evaluation audits using checklists, sampling plans, and calibration sessions.
• Document edge cases and elevate guideline gaps to reduce annotator disagreement.
• Support dataset curation, labeling, and QC for training data quality.

Connaissances

Data labeling

Attention to detail

Follow guidelines

Written communication

Inter-annotator agreement

Description du poste

Rex.zone connects you with remote data annotator roles focused on producing high-quality training data and evaluation signals for AI/ML systems. You will support LLM training pipelines and computer vision workflows through data labeling, RLHF preference ranking, prompt-response evaluation, and QA evaluation to improve model performance and reliability.

The work involves labeling for NLP tasks like text classification and named entity recognition, RLHF comparisons, rubric-based scoring, prompt