Remote Data Labeling Specialist (Portland)

<strong>Remote Data Labeling Jobs in Portland<br><br></strong>Rex.zone is hiring a <strong>Remote Data Labeling Specialist (Portland)</strong> to create and evaluate training data for modern AI systems. You will label and review text, images, and multimodal content used in <strong>LLM evaluation</strong>, <strong>RLHF</strong> workflows, and <strong>computer vision annotation</strong>, collaborating asynchronously with QA leads and ML teams.<br><br><strong>What You Will Do<br><br></strong><ul><li>Execute data labeling and data annotation across NLP and computer vision workflows (NER, classification, ranking, structured extraction)</li><li>Support RLHF via preference labeling, rubric scoring, prompt evaluation, and response quality judgments</li><li>Perform QA evaluation: audit labeled datasets, resolve edge cases, and enforce annotation guidelines compliance</li><li>Contribute to content safety labeling (toxicity, self-harm, regulated topics) with policy-based tagging</li><li>Document decisions, escalate ambiguous examples, and help refine instructions to improve training data quality<br><br></li></ul><strong>Project Types You May Support<br><br></strong><ul><li>LLM training pipelines: prompt/response grading, factuality checks, instruction-following evaluation</li><li>RLHF: pairwise preference labeling, reward model data generation, disagreement resolution</li><li>NLP: named entity recognition, intent classification, sentiment/stance labeling</li><li>Computer vision: bounding boxes, polygons, keypoints, attribute tagging</li><li>QA evaluation: sampling plans, inter-annotator agreement checks, targeted rework<br><br></li></ul><strong>Requirements<br><br></strong><ul><li>Mid-Senior experience in data labeling, data annotation, or QA evaluation for ML datasets</li><li>Ability to follow complex annotation guidelines, handle edge cases, and maintain consistent quality</li><li>Familiarity with NLP (e.g., named entity recognition) and/or computer vision annotation fundamentals</li><li>LLM evaluation and prompt evaluation experience preferred; RLHF exposure is a plus</li><li>Strong written communication and ability to collaborate asynchronously in a remote environment<br><br></li></ul><strong>Work Model & Location<br><br></strong>This is a <strong>Remote</strong>, <strong>FULL_TIME</strong> role aligned to Portland, Oregon candidates or those seeking Portland-aligned remote opportunities. Work is performed online using secure annotation tools and task platforms.<br><br><strong>Pay<br><br></strong>$30–$50 per hour (hourly base pay range).