Remote Data Annotator Phoenix

Remote data annotator jobs in Phoenix e focus on producing high-quality training data for AI systems across NLP, computer vision, and LLM training pipelines. You will perform data labeling, RLHF-based preference ranking, prompt evaluation, and QA evaluation to improve model performance and training data quality.

• Label data for NLP tasks such as text classification, intent, sentiment, toxicity, and named entity recognition
• Perform large language model evaluation by scoring relevance, factuality, completeness, instruction-following, and safety
• Execute RLHF-style preference ranking by comparing and ranking model outputs
• Annotate computer vision data using bounding boxes, polygons, keypoints, and segmentation
• Complete QA evaluation using audits, inter-annotator agreement targets, and rework when needed
• Follow annotation guidelines compliance, document edge cases, and support calibration sessions

• Strong written English and attention to detail
• Ability to follow complex guidelines and self-audit work for consistency and accuracy
• Preferred: experience with data labeling tools, prompt evaluation, content safety labeling, or QA processes

This is a full-time Remote role performed online with secure workflows. Open to candidates in the US, including Phoenix-based applicants.