Senior Data Labeling Jobs in Miami

Senior data labeling jobs in Miami at Rex.zone focus on high-accuracy training data creation and evaluation for AI/ML 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.

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Senior Data Labeling Jobs in Miami — LinkedIn Job Metadata

Title: Senior Data Labeling Specialist (Miami) 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: Data Labeling, Data Annotation, RLHF, QA Evaluation, Prompt Evaluation, Named Entity Recognition, Computer Vision Annotation, Content Safety Labeling, LLM Evaluation, Annotation Guidelines Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

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.

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/online evaluation metrics.

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.

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.

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.

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.

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.

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).

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.

Frequently Asked Questions

  • Q: Are these senior data labeling jobs in Miami remote?

    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.

  • Q: What makes this role “senior” in data labeling?

    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.

  • Q: What types of tasks are included (NLP, CV, safety, RLHF)?

    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.

  • Q: Do I need engineering experience for the Job Function listed as Engineering?

    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.

  • Q: What skills should I emphasize to match the job intent?

    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.

  • Q: How is quality evaluated in data labeling work?

    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.

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