Senior Data Annotator (Portland, Remote)

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<strong>Job Opening: Senior Data Annotator (Portland, Remote)<br><br></strong>Rex.zone is hiring a Senior Data Annotator to produce high-quality labeled data and evaluation signals used to train and assess AI/ML models. You will support LLM training pipelines, RLHF preference data, prompt evaluation, QA evaluation audits, and content safety labeling while documenting edge cases to improve training data quality and model performance.<br><br><strong>What You Will Do<br><br></strong><ul><li>Perform expert-level data labeling across modalities: NLP text classification, named entity recognition, summarization checks, and dialogue evaluation for LLMs</li><li>Create RLHF preference judgments and rationales for pairwise ranking, instruction following, and helpfulness/harmlessness criteria</li><li>Run QA evaluation workflows: spot checks, inter-annotator agreement reviews, adjudication support, and error taxonomies</li><li>Annotate computer vision data when needed (bounding boxes, polygons, keypoints) and verify label accuracy against guidelines</li><li>Conduct prompt evaluation and model output grading for factuality, safety, tone, policy compliance, and refusal quality</li><li>Document edge cases, propose guideline updates, and provide clear feedback to improve dataset consistency<br><br></li></ul><strong>Required Qualifications<br><br></strong><ul><li>Mid-Senior experience in data annotation, data labeling, or QA evaluation in production environments</li><li>Ability to interpret and apply complex annotation guidelines with high consistency</li><li>Hands-on experience with LLM evaluation, RLHF, prompt evaluation, or human feedback workflows</li><li>Strong written reasoning for explaining labels, preferences, and safety decisions</li><li>Comfort working remotely with versioned instructions, batch tracking, and deadlines<br><br></li></ul><strong>Employment Details<br><br></strong><ul><li>Workplace: Remote (Portland-based candidates welcome; work is performed remotely)</li><li>Employment Type: Full-time</li><li>Compensation: $30–$50 per hour</li></ul>

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