[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-ai-data-annotation-jobs-miami":3},{"Ques":4,"Slug":31,"Header":32,"job_category":62},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"Yes. Remote Type is Remote, and the work is performed remotely while being listed for Miami-based search intent and US eligibility.","Are these remote AI data annotation jobs in Miami fully remote?",{"A":11,"Q":12},"Common tasks include data labeling for NLP (classification, named entity recognition), computer vision annotation (bounding boxes, segmentation), content safety labeling, prompt evaluation, and QA evaluation of training data quality.","What kinds of tasks are included in AI data annotation?",{"A":14,"Q":15},"RLHF (Reinforcement Learning from Human Feedback) is a process where human preferences and rubric-based scores are used to improve model behavior. In this role, you may rank responses, score outputs, and provide structured feedback used in LLM training pipelines.","What is RLHF and how does it relate to this job?",{"A":17,"Q":18},"This posting is for FULL_TIME employment. The page also references contract and freelance as common search modifiers, but the role metadata remains full-time.","Is this role full-time or contract?",{"A":20,"Q":21},"Emphasize AI data annotation, data labeling, RLHF, LLM evaluation, prompt evaluation, QA evaluation, annotation guidelines compliance, training data quality, named entity recognition, computer vision annotation, and content safety labeling.","What skills should I emphasize for remote AI data annotation roles?",{"A":23,"Q":24},"QA evaluation includes auditing labeled samples, checking guideline adherence, analyzing disagreement patterns, and correcting label errors to improve training data quality and downstream model performance improvement.","What does quality assurance mean in annotation work?",{"A":26,"Q":27},"Projects commonly support AI labs, tech startups, enterprise AI teams, BPOs, and annotation vendors that build and evaluate datasets for NLP, computer vision, and content safety systems.","Who hires for roles like this through Rex.zone?",{"A":29,"Q":30},"High-quality labels, RLHF preference data, and prompt evaluation results help measure and improve instruction following, safety, and reliability, which directly affects large language model evaluation outcomes.","How does this work impact large language model evaluation?","remote-ai-data-annotation-jobs-miami",{"desc":33,"title":34,"content":35},"Remote AI data annotation jobs in Miami at Rex.zone focus on training data creation for modern AI\u002FML systems. You will label and review text, images, audio, and video to improve large language model evaluation, RLHF feedback quality, and computer vision annotation accuracy. This role supports real-world LLM training pipelines, prompt evaluation, content safety labeling, and QA evaluation workflows used by AI labs, tech startups, and annotation vendors. If you are looking for full-time remote work that strengthens model performance improvement through training data quality and annotation guidelines compliance, explore and apply via Rex.zone.","Remote AI Data Annotation Jobs in Miami",[36,38,41,44,47,50,53,56,59],{"h2":34,"desc":37},"Title: Remote AI Data Annotation Specialist (Miami)\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: AI data annotation, data labeling, RLHF, LLM evaluation, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, annotation guidelines, training data quality\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":39,"desc":40},"About the Role","You will work remotely from Miami (or anywhere in the US) to produce and evaluate high-quality training datasets for AI\u002FML systems. Daily work includes data labeling across NLP and computer vision tasks, RLHF preference ranking, prompt evaluation for large language model evaluation, and QA evaluation to ensure annotation guidelines compliance. You will collaborate with cross-functional stakeholders to improve training data quality, reduce ambiguity in labeling instructions, and drive model performance improvement through consistent, defensible annotation decisions.",{"h2":42,"desc":43},"Key Responsibilities","[\"Annotate and review text datasets for NLP tasks such as named entity recognition, classification, summarization quality checks, and reasoning consistency\",\"Perform RLHF tasks including preference ranking, rubric-based scoring, and evaluation of assistant responses for helpfulness, honesty, and harmlessness\",\"Conduct prompt evaluation and QA evaluation to identify edge cases, label noise, and guideline gaps that affect large language model evaluation\",\"Label computer vision data (bounding boxes, polygons, keypoints, segmentation masks) and validate inter-annotator agreement for accuracy\",\"Apply content safety labeling and policy-based moderation labels for sensitive content categories and safety risk tiers\",\"Document decisions, propose improvements to annotation guidelines, and support calibration sessions to improve consistency\",\"Track quality metrics (precision\u002Frecall proxies, disagreement rates, audit pass rates) and contribute to training data quality initiatives\"]",{"h2":45,"desc":46},"Required Qualifications","[\"Mid-Senior experience in AI data annotation, data labeling, QA evaluation, or related data operations roles\",\"Hands-on familiarity with LLM evaluation concepts, RLHF workflows, and rubric-based scoring\",\"Strong written communication and ability to justify labeling decisions with clear rationale\",\"Ability to follow annotation guidelines compliance requirements and maintain high attention to detail\",\"Comfort working in remote, metrics-driven production environments with iterative feedback\"]",{"h2":48,"desc":49},"Preferred Qualifications","[\"Experience with named entity recognition, taxonomy design, and error analysis for NLP datasets\",\"Exposure to computer vision annotation tools and segmentation\u002Fbox quality standards\",\"Prior work on content safety labeling, policy interpretation, or trust-and-safety style evaluation\",\"Understanding of LLM training pipelines and how data quality impacts model performance improvement\",\"Experience collaborating with AI labs, tech startups, BPOs, or annotation vendors\"]",{"h2":51,"desc":52},"What You Will Work On","[\"Large language model evaluation for instruction following, reasoning quality, and factuality checks\",\"RLHF preference data and prompt evaluation to improve alignment and user experience\",\"Training data quality audits to reduce label noise and guideline drift\",\"Multi-modal annotation projects spanning NLP, computer vision annotation, and content safety labeling\"]",{"h2":54,"desc":55},"Compensation and Benefits","[\"Salary range: 63360 to 126720 USD per year\",\"Full-time remote role (Remote Type: Remote)\",\"Structured onboarding, calibration, and ongoing QA evaluation feedback loops\",\"Opportunities to work across NLP, computer vision annotation, and LLM evaluation programs\"]",{"h2":57,"desc":58},"How to Apply on Rex.zone","[\"Prepare a short summary of your annotation experience (NLP, computer vision annotation, RLHF, QA evaluation)\",\"Highlight familiarity with annotation guidelines compliance and training data quality practices\",\"Apply through Rex.zone and complete any required screening tasks for large language model evaluation or data labeling\"]",{"h2":60,"desc":61},"Search Modifiers and Related Work Types","[\"remote\",\"full-time\",\"contract\",\"freelance\",\"entry-level\",\"senior\",\"NLP\",\"computer vision\",\"content safety\",\"LLM training pipelines\",\"AI labs\",\"tech startups\",\"BPOs\",\"annotation vendors\"]","AI Data Operations"]