[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-labeling-jobs-north-america":3},{"Ques":4,"Slug":31,"Header":32,"job_category":48},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"Senior data labeling jobs in North America focus on producing and reviewing high-quality labeled data used to train and evaluate AI models. The work commonly includes data labeling, QA evaluation, RLHF preference labeling, prompt evaluation, named entity recognition, content safety labeling, and sometimes computer vision annotation, all aimed at improving training data quality and model performance.","What are senior data labeling jobs in North America?",{"A":11,"Q":12},"Yes. The role is explicitly Remote and FULL_TIME, aligned to North America collaboration requirements.","Is this role remote and full-time?",{"A":14,"Q":15},"RLHF (Reinforcement Learning from Human Feedback) is a workflow where humans provide preference signals—such as ranking two model responses or scoring outputs against a rubric. These labels become training and evaluation signals that help improve large language model behavior, making RLHF evaluation and preference labeling core tasks in senior data labeling roles.","What is RLHF and why is it part of this job?",{"A":17,"Q":18},"You may use web-based annotation tools and QA dashboards to label and review text classification, named entity recognition, prompt evaluation, and conversation evaluation. Depending on project needs, you may also support computer vision annotation (boxes, polygons, keypoints) or content safety labeling with policy-based rubrics.","What tools or tasks might I use day to day?",{"A":20,"Q":21},"Success includes consistent annotation guidelines compliance, strong QA evaluation results, improved training data quality, reduced rework, and clear documentation and calibration practices that generate reliable signals for model performance improvement in LLM training pipelines.","What does success look like in this role?",{"A":23,"Q":24},"Openings may support AI labs, technology companies, startups, annotation vendors, and BPO delivery partners running production AI\u002FML data operations, including NLP, computer vision, and content safety labeling programs.","Who hires for these roles through Rex.zone?",{"A":26,"Q":27},"This posting is for FULL_TIME remote work. Rex.zone may also list contract or freelance data labeling roles separately depending on project needs.","Do you offer contract or freelance options?",{"A":29,"Q":30},"Apply via Rex.zone and include details on data labeling, data annotation, QA evaluation, RLHF, LLM evaluation, and any experience with named entity recognition, computer vision annotation, or content safety labeling.","How do I apply?","senior-data-labeling-jobs-north-america",{"desc":33,"title":34,"content":35},"Senior data labeling jobs in North America at Rex.zone focus on building high-quality training datasets for AI\u002FML systems through data labeling, RLHF evaluation, and QA review. You will apply annotation guidelines compliance to improve training data quality for LLM training pipelines, NLP and computer vision models, and content safety labeling workflows. This remote, full-time role supports model performance improvement via prompt evaluation, named entity recognition, and systematic error analysis in production annotation operations. Explore and apply through Rex.zone to join teams powering real-world AI products across AI labs, tech startups, annotation vendors, and BPO delivery partners.","Senior Data Labeling Jobs in North America",[36,39,42,45],{"h2":37,"desc":38},"Senior Data Labeling Specialist (North America, Remote)","Title: Senior Data Labeling Specialist (North America, Remote)\nDate: 25-02-2026\nCompany: Rex.zone\nCountry: US\nRemote Type: Remote\nEmployment Type: FULL_TIME\nExperience Level: Mid-Senior\nIndustry: Technology\nJob Function: Engineering\nSkills: data labeling, data annotation, RLHF, LLM evaluation, prompt evaluation, QA evaluation, training data quality, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, dataset curation\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR\n\nYou will lead day-to-day senior labeling execution and quality assurance across NLP, LLM evaluation, and computer vision annotation projects. This role requires strong judgment, consistent rubric use, and the ability to translate ambiguous model behaviors into clear labels that increase model performance.\n\nKey Responsibilities:\n- Own training data quality across multiple queues by enforcing annotation guidelines compliance and resolving edge cases\n- Perform RLHF-style ranking, preference labeling, and rubric-based QA evaluation for large language model evaluation\n- Conduct prompt evaluation and response assessment for helpfulness, factuality, toxicity, and policy compliance (content safety labeling)\n- Label and review tasks including named entity recognition, text classification, summarization grading, and conversation evaluation\n- Support computer vision annotation programs (bounding boxes, polygons, keypoints) with strict quality checks when required\n- Run error analysis, identify systematic label noise, and propose guideline updates to reduce ambiguity\n- Mentor labelers, calibrate decision-making, and maintain inter-annotator agreement targets\n- Create and maintain lightweight documentation for task definitions, examples, and escalation paths\n- Partner with operations and engineering stakeholders to ensure smooth throughput, QA gates, and audit readiness\n\nQualifications:\n- Experience in production data labeling or data annotation with demonstrated QA review ownership\n- Comfort with large language model evaluation, rubric design, and ranking \u002F preference labeling (RLHF workflows)\n- Strong written reasoning skills and attention to detail in long-form prompt evaluation\n- Familiarity with annotation tools, dataset curation, and quality metrics (precision\u002Frecall proxies, agreement rates)\n- Ability to work independently in a remote environment with consistent delivery and documentation\n\nPreferred:\n- Exposure to NLP tasks such as named entity recognition, intent classification, and information extraction\n- Exposure to computer vision annotation or multimodal evaluation\n- Experience supporting content safety labeling programs and policy-aligned evaluation\n\nWhat You’ll Deliver:\n- Higher training data quality and reduced rework through clear decisions and reliable QA evaluation\n- Better model performance improvement signals through consistent RLHF preference labeling and prompt evaluation\n- Durable annotation guidelines compliance via calibration, documentation, and feedback loops\n\nHow to Apply:\n- Apply through Rex.zone with a resume highlighting data labeling, QA evaluation, RLHF, and LLM evaluation experience",{"h2":40,"desc":41},"What You’ll Work On","This senior data labeling role spans LLM training pipelines and evaluation workflows used by AI labs, tech startups, annotation vendors, and BPO delivery teams. Typical work includes RLHF preference judgments, prompt evaluation, content safety labeling, named entity recognition, and computer vision annotation with a focus on training data quality and annotation guidelines compliance.",{"h2":43,"desc":44},"Quality Standards and Evaluation","You will be measured on training data quality, consistency of rubric application, and the ability to reduce ambiguity in guidelines. Success includes strong QA evaluation outcomes, stable inter-annotator agreement, low defect rates, and clear documentation that improves model performance improvement signals for large language model evaluation.",{"h2":46,"desc":47},"Remote Work Expectations (North America)","This is a Remote, FULL_TIME position aligned to North America collaboration needs. You will operate with structured queues, clear QA gates, and asynchronous communication, while participating in calibration sessions to keep annotation guidelines compliance and training data quality consistent across projects.","AI Data Operations"]