[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-ai-data-annotation-jobs-miami":3},{"Ques":4,"Slug":25,"Header":26,"job_category":56},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"Daily work includes data labeling and QA evaluation using strict rubrics, RLHF preference judgments, prompt evaluation, content safety labeling, and training data quality audits. You may also document edge cases, suggest rubric updates, and support dataset curation for LLM training pipelines.","What does a senior AI data annotation professional do day to day?",{"A":11,"Q":12},"Yes. The Remote Type is Remote. The Miami modifier reflects hiring demand and search intent, while the work is performed remotely within the US context.","Is this role remote even though it targets Miami?",{"A":14,"Q":15},"Keyword-aligned skills include AI data annotation, data labeling, RLHF, LLM evaluation, prompt evaluation, QA evaluation, training data quality, annotation guidelines compliance, named entity recognition, content safety labeling, computer vision annotation, and dataset curation.","What skills best match senior AI data annotation jobs in Miami?",{"A":17,"Q":18},"Common employer types include AI labs, tech startups, enterprise AI teams, BPOs, and annotation vendors running large-scale LLM training pipelines and evaluation programs.","What kinds of AI teams hire for these roles through Rex.zone?",{"A":20,"Q":21},"It is strongly preferred for senior roles. If you have related experience in rubric-based evaluation, pairwise ranking, or high-stakes QA evaluation, you can often ramp into RLHF workflows through calibration and guideline training.","Do I need prior RLHF experience?",{"A":23,"Q":24},"The listed salary range is USD 63360 to USD 126720 per YEAR for this FULL_TIME role. Specific offers depend on experience level, evaluation complexity, and performance expectations.","What is included in the compensation range?","senior-ai-data-annotation-jobs-miami",{"desc":27,"title":28,"content":29},"Senior AI data annotation jobs in Miami focus on expert data labeling and large language model evaluation that directly improves training data quality, annotation guidelines compliance, and model performance improvement. At Rex.zone, you will support AI\u002FML training workflows across RLHF, prompt evaluation, QA evaluation, and content safety labeling, partnering with AI labs, tech startups, and annotation vendors. This remote, full-time role emphasizes rigorous rubric-based judgment, dataset curation, and end-to-end LLM training pipelines, including NLP and computer vision annotation where needed. If you are seeking remote senior roles tied to real-world production model development, explore and apply through Rex.zone.","Senior AI Data Annotation Jobs in Miami",[30,32,35,38,41,44,47,50,53],{"h2":28,"desc":31},"Date: 25-02-2026 | Company: Rex.zone | Country: US | Remote Type: Remote | Employment Type: FULL_TIME | Experience Level: Mid-Senior | Industry: Technology | Job Function: Engineering | Skills: AI data annotation, data labeling, RLHF, LLM evaluation, prompt evaluation, QA evaluation, training data quality, annotation guidelines compliance, named entity recognition, content safety labeling, computer vision annotation, dataset curation | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR",{"h2":33,"desc":34},"About the Role","You will deliver high-precision annotations and evaluations for AI\u002FML systems, with a focus on large language model evaluation and RLHF-style preference judgments. Work includes applying detailed rubrics, resolving ambiguous edge cases, writing clear rationales, and auditing training data quality to reduce label noise. You will support production LLM training pipelines by improving annotation guidelines compliance, identifying failure modes, and partnering with QA to drive model performance improvement.",{"h2":36,"desc":37},"What You Will Do","Core workflows include: (1) Perform data labeling for NLP tasks such as named entity recognition, classification, summarization quality checks, and instruction-following evaluation, (2) Conduct RLHF evaluations including pairwise ranking, preference labeling, and feedback rationales, (3) Execute prompt evaluation and QA evaluation for helpfulness, correctness, and policy adherence, (4) Run content safety labeling for sensitive content, toxicity, and policy violations, (5) Contribute to dataset curation by flagging low-quality sources, duplicates, and leakage risks, (6) Validate annotation guidelines compliance and propose rubric improvements based on recurring errors, (7) Collaborate with cross-functional teams to align labeling targets with model training objectives.",{"h2":39,"desc":40},"Domains You May Support","Projects may span: NLP evaluation, LLM instruction tuning support, retrieval-augmented generation checks, content safety labeling, multilingual evaluation, and computer vision annotation (bounding boxes, segmentation, attribute tagging) when required. You may also support prompt-response grading for factuality, groundedness, hallucination detection, and style\u002Fformat adherence.",{"h2":42,"desc":43},"Requirements","You should have mid-senior experience in data annotation or AI evaluation, strong attention to detail, and the ability to apply consistent judgments under complex rubrics. Familiarity with RLHF concepts, prompt evaluation, training data quality practices, and QA evaluation methods is expected. You must communicate clearly in written form, document edge cases, and maintain high throughput without sacrificing accuracy.",{"h2":45,"desc":46},"Preferred Qualifications","Experience with LLM evaluation rubrics, policy-based content safety labeling, and dataset curation for instruction tuning. Exposure to named entity recognition guidelines, ambiguity resolution, inter-annotator agreement, and calibration sessions. Familiarity with computer vision annotation tools or multimodal evaluation is a plus.",{"h2":48,"desc":49},"How Success Is Measured","Success metrics include annotation accuracy, consistency (agreement and calibration), adherence to annotation guidelines compliance, quality of rationales, speed with sustained training data quality, and contributions that measurably support model performance improvement in downstream evaluations.",{"h2":51,"desc":52},"Remote Work and Collaboration","This is a Remote, FULL_TIME role based in the US market and aligned with Miami talent demand. You will collaborate asynchronously with reviewers, QA, and project leads, participate in calibration sessions, and follow secure data handling requirements.",{"h2":54,"desc":55},"Apply via Rex.zone","To explore this Senior AI Data Annotation role and related remote, contract, freelance, entry-level, and senior opportunities across NLP, computer vision, content safety, and LLM training pipelines, apply through Rex.zone and keep your profile updated with relevant evaluation and data labeling experience.","AI Data Operations"]