[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-labeling-jobs-miami":3},{"Ques":4,"Slug":25,"Header":26,"job_category":60},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"Yes. This posting is explicitly marked Remote and FULL_TIME, and work is performed remotely while supporting teams and projects that may be aligned to Miami and broader US operations.","Are these senior data labeling jobs in Miami remote?",{"A":11,"Q":12},"Senior scope includes handling complex edge cases, leading QA evaluation and adjudication, improving annotation guidelines compliance, mentoring calibration, and using error analysis to improve training data quality and downstream model performance improvement.","What makes this role “senior” in data labeling?",{"A":14,"Q":15},"Tasks commonly include named entity recognition and other NLP labeling, computer vision annotation, content safety labeling, prompt evaluation, and RLHF preference ranking and rubric-based grading for large language model evaluation.","What types of tasks are included (NLP, CV, safety, RLHF)?",{"A":17,"Q":18},"You do not need to be a software engineer, but you should be comfortable with structured workflows, quality metrics, tool-based annotation processes, and collaborating with engineering or research stakeholders to support LLM training pipelines.","Do I need engineering experience for the Job Function listed as Engineering?",{"A":20,"Q":21},"Emphasize data labeling, data annotation, QA evaluation, RLHF, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, LLM evaluation, and annotation guidelines writing and compliance.","What skills should I emphasize to match the job intent?",{"A":23,"Q":24},"Quality is evaluated through audit sampling, defect categorization, adjudication, inter-annotator agreement, calibration outcomes, and demonstrated improvements in training data quality that support better model performance improvement.","How is quality evaluated in data labeling work?","senior-data-labeling-jobs-miami",{"desc":27,"title":28,"content":29},"Senior data labeling jobs in Miami at Rex.zone focus on high-accuracy training data creation and evaluation for AI\u002FML systems. In this remote, full-time role, you will lead annotation workflows across NLP, computer vision, and content safety, applying RLHF, prompt evaluation, and QA evaluation to improve large language model evaluation outcomes. You will translate ambiguous model behaviors into clear annotation guidelines compliance, audit training data quality, and drive model performance improvement through systematic review, adjudication, and error analysis. Explore and apply through Rex.zone to join AI labs, tech startups, BPOs, and annotation vendors building reliable LLM training pipelines.","Senior Data Labeling Jobs in Miami",[30,33,36,39,42,45,48,51,54,57],{"h2":31,"desc":32},"Senior Data Labeling Jobs in Miami — LinkedIn Job Metadata","Title: Senior Data Labeling 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: Data Labeling, Data Annotation, RLHF, QA Evaluation, Prompt Evaluation, Named Entity Recognition, Computer Vision Annotation, Content Safety Labeling, LLM Evaluation, Annotation Guidelines\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":34,"desc":35},"About the Role","You will lead end-to-end data labeling and evaluation workflows for AI training datasets used in large language models and multimodal systems. You will handle complex edge cases, calibrate labeler performance, and ensure annotation guidelines compliance across projects involving named entity recognition, prompt evaluation, content safety labeling, and computer vision annotation. Your work will directly impact training data quality, model performance improvement, and trustworthy LLM evaluation results across production-grade LLM training pipelines.",{"h2":37,"desc":38},"What You Will Do","Core responsibilities include: (1) perform advanced data annotation and data labeling across NLP, CV, and safety domains; (2) execute RLHF tasks such as preference ranking, rubric-based scoring, and justification writing; (3) run QA evaluation via sampling, audits, adjudication, and inter-annotator agreement; (4) conduct error analysis and root-cause investigations on label noise and guideline gaps; (5) improve annotation guidelines with examples, decision trees, and edge-case handling; (6) collaborate with engineering and research teams to align labels with downstream training objectives and offline\u002Fonline evaluation metrics.",{"h2":40,"desc":41},"Domains You May Work On","Common workstreams include: (1) NLP labeling such as NER, intent, sentiment, and taxonomy mapping; (2) large language model evaluation with prompt evaluation, response grading, and hallucination detection; (3) RLHF preference datasets and safety alignment; (4) computer vision annotation including bounding boxes, polygons, keypoints, segmentation, and OCR; (5) content safety labeling for policy compliance, harmful content detection, and red-team style data collection.",{"h2":43,"desc":44},"Required Qualifications","You should have: (1) experience in data labeling or data annotation with demonstrated QA evaluation ownership; (2) strong written English and the ability to apply consistent rubrics for LLM evaluation and prompt evaluation; (3) ability to resolve ambiguous cases using clear reasoning and documented decisions; (4) familiarity with annotation tools, audit workflows, and training data quality processes; (5) comfort working remotely in a metric-driven production environment.",{"h2":46,"desc":47},"Preferred Qualifications","Nice-to-haves include: (1) experience with RLHF datasets, preference modeling data, or rubric calibration; (2) experience with named entity recognition projects, ontology building, or schema design; (3) computer vision annotation experience (segmentation, polygons, keypoints); (4) experience with content safety labeling and policy interpretation; (5) ability to translate model failure modes into actionable guideline updates that support model performance improvement.",{"h2":49,"desc":50},"Quality Standards and How Success Is Measured","Success is measured through training data quality and reliability signals such as: (1) audit pass rate and defect density; (2) annotation guidelines compliance and edge-case resolution accuracy; (3) inter-annotator agreement improvements and adjudication outcomes; (4) turnaround time with consistent quality; (5) demonstrated impact on model performance improvement via cleaner labels, better RLHF preference signal, and more stable large language model evaluation results.",{"h2":52,"desc":53},"Remote Work and Collaboration","This is a Remote, FULL_TIME role supporting projects that may be associated with teams in Miami and across the US. You will collaborate asynchronously with reviewers, project leads, and engineering partners, participate in calibration sessions, and document labeling decisions to keep workflows consistent across distributed teams.",{"h2":55,"desc":56},"Who Hires for These Roles","Rex.zone roles may support employer types including AI labs, tech startups, BPOs, annotation vendors, and enterprise AI teams. Project domains may include NLP, computer vision, content safety, and LLM training pipelines, with opportunities spanning remote, contract, freelance, and full-time work depending on project needs (this posting is Remote and FULL_TIME).",{"h2":58,"desc":59},"How to Apply on Rex.zone","Apply through Rex.zone with a resume highlighting data labeling, QA evaluation, RLHF, prompt evaluation, and domain experience (NLP, computer vision annotation, content safety labeling). Include examples of guideline writing, adjudication decisions, audit work, and any measurable improvements you made to training data quality or model performance improvement.","AI Data Operations"]