[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-data-annotation-jobs-north-america":3},{"Ques":4,"Slug":28,"Header":29,"job_category":57},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25],{"A":8,"Q":9},"They are Remote roles where you label and evaluate training data used in AI\u002FML systems. Work may include data labeling for NLP, computer vision annotation, content safety labeling, prompt evaluation, and RLHF evaluation to improve large language model behavior.","What are remote data annotation jobs in North America?",{"A":11,"Q":12},"This posting is for FULL_TIME Remote employment. Rex.zone may also host contract or freelance annotation projects, but this job is explicitly full-time.","Is this role full-time or contract\u002Ffreelance?",{"A":14,"Q":15},"RLHF (Reinforcement Learning from Human Feedback) uses human preference judgments to improve model outputs. In this role, you may rank responses, apply rubrics, and provide structured evaluations that feed into LLM training pipelines.","What is RLHF and how is it used here?",{"A":17,"Q":18},"Typical tasks include annotation guidelines compliance checks, training data quality audits, QA evaluation, named entity recognition, prompt evaluation, computer vision annotation (when applicable), and content safety labeling for policy categories.","What types of tasks will I do day to day?",{"A":20,"Q":21},"Not always, but you should be comfortable with structured workflows, detailed rubrics, and quality metrics. This posting is aligned to Job Function: Engineering because it supports production AI\u002FML training pipelines and evaluation processes.","Do I need an engineering background to qualify?",{"A":23,"Q":24},"Rex.zone is the platform where you access tasks, guidelines, calibration updates, and QA feedback. It anchors the remote workflow for submitting labeled data and evaluation results.","How does Rex.zone fit into the workflow?",{"A":26,"Q":27},"Strong candidates consistently follow annotation guidelines, handle edge cases, maintain high audit scores, communicate clearly, and demonstrate experience with data labeling, QA evaluation, and evaluation workflows tied to model performance improvement.","What makes a strong candidate for these remote data annotation jobs?","remote-data-annotation-jobs-north-america",{"desc":30,"title":31,"content":32},"Remote data annotation jobs in North America at Rex.zone focus on creating and evaluating training data for AI\u002FML systems, including LLM training pipelines, RLHF, data labeling, and QA evaluation. As a Data Annotation Specialist, you will apply annotation guidelines compliance to produce high-quality labeled datasets used to improve model performance, reduce hallucinations, and increase safety across NLP, computer vision annotation, and content safety labeling. You will complete prompt evaluation tasks, named entity recognition, intent classification, and multimodal labeling workflows while collaborating with QA and operations teams. Explore full-time remote roles designed for consistent throughput, training data quality, and measurable impact on production model behavior.","Remote Data Annotation Jobs in North America",[33,36,39,42,45,48,51,54],{"h2":34,"desc":35},"Remote Data Annotation Specialist (North America)","Title: Remote Data Annotation Specialist (North America)\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: remote data annotation, data labeling, RLHF, QA evaluation, prompt evaluation, training data quality, annotation guidelines compliance, named entity recognition, NLP, computer vision annotation, content safety labeling, LLM training pipelines\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":37,"desc":38},"About the Role","You will deliver remote data annotation for North America programs supporting AI labs, tech startups, and annotation vendors using Rex.zone. Your day-to-day work includes labeling text, image, and multimodal data; performing prompt evaluation for LLM outputs; and executing RLHF preference ranking to improve helpfulness, harmlessness, and instruction-following. You will follow detailed labeling rubrics, maintain annotation guidelines compliance, and partner with QA to uphold training data quality that drives model performance improvement.",{"h2":40,"desc":41},"What You Will Do","Core workflows include: (1) data labeling for NLP tasks such as named entity recognition, classification, summarization checks, and intent detection; (2) RLHF evaluation including pairwise comparisons, rationale selection, and rubric-based scoring; (3) QA evaluation using audits, inter-annotator agreement, and error taxonomy feedback; (4) computer vision annotation such as bounding boxes, polygons, keypoints, and caption verification; (5) content safety labeling for policy categories, sensitive content, and risk severity; (6) prompt evaluation and red-team style assessments to identify failure modes and improve model behavior.",{"h2":43,"desc":44},"Required Qualifications","You have experience executing remote data annotation or data labeling with consistent quality and throughput. You can interpret complex annotation guidelines, apply edge-case reasoning, and document decisions clearly for QA evaluation. You are comfortable working with LLM training pipelines concepts, including RLHF, prompt evaluation, and dataset iteration. You communicate well in writing, manage tasks independently in a remote setting, and deliver reliable outputs in full-time workflows.",{"h2":46,"desc":47},"Preferred Qualifications","Experience with named entity recognition, ontology design, or taxonomy-based labeling. Familiarity with computer vision annotation tools and verification workflows. Exposure to content safety labeling, policy interpretation, and harm mitigation. Understanding of evaluation metrics such as precision\u002Frecall basics, inter-annotator agreement, and rubric calibration. Prior work supporting AI labs, tech startups, BPOs, or annotation vendors is helpful.",{"h2":49,"desc":50},"Quality Standards and How Success Is Measured","Success is measured by training data quality, annotation guidelines compliance, audit pass rate, and consistency across edge cases. You will receive calibration feedback through QA evaluation, error tagging, and targeted re-training. Strong performance includes stable inter-annotator agreement, low defect rates, and clear notes that accelerate model performance improvement through dataset iteration.",{"h2":52,"desc":53},"Work Environment (Remote, North America)","This is a Remote, FULL_TIME role aligned to North America time zones where required by program needs. You will collaborate asynchronously through Rex.zone workflows, follow queue-based task assignments, and participate in calibration sessions for RLHF and evaluation tasks. Remote roles remain explicitly Remote; some programs may also offer contract or freelance options on Rex.zone, but this posting is for full-time hiring.",{"h2":55,"desc":56},"How to Apply","Apply through Rex.zone to be considered for remote data annotation jobs in North America. Your application should highlight prior data labeling experience, QA evaluation exposure, familiarity with RLHF or prompt evaluation, and examples of following detailed annotation guidelines. If selected, you may complete a short skills assessment focused on training data quality and rubric adherence.","AI Data Operations"]