Remote Data Labeling Jobs in Miami

Remote data labeling jobs in Miami are mid-senior roles focused on creating high-quality training data for AI systems used in real-world NLP and computer vision products. On Rex.zone, you will label and evaluate text, images, and conversations to support large language model evaluation, RLHF workflows, and training data quality improvement. This job supports AI/ML training pipelines through annotation guidelines compliance, QA evaluation, prompt evaluation, and content safety labeling so models become safer, more accurate, and more useful. If you want full-time remote work aligned with Miami talent, apply through Rex.zone to join projects for AI labs, tech startups, and annotation vendors.

Remote Data Labeling Jobs in Miami — LinkedIn Job Metadata

About the Role

You will work remotely from the US (Miami talent welcome) to produce and validate labeled datasets that improve model performance in NLP, computer vision, and LLM training. Your day-to-day work includes applying detailed annotation guidelines, performing QA evaluation for consistency, and completing prompt evaluation and response ranking tasks used in RLHF (Reinforcement Learning from Human Feedback). You will partner with reviewers and ops leads to resolve edge cases, document decision rationales, and ensure training data quality across batches destined for production-grade AI/ML training pipelines.

What You Will Do

You will label and evaluate text and chat data for large language model evaluation, including preference ranking and rubric-based grading; perform named entity recognition and entity linking for structured NLP datasets; annotate images and video frames for computer vision annotation tasks such as bounding boxes, polygons, keypoints, and attribute tagging; complete content safety labeling for policy categories (hate, harassment, self-harm, sexual content, violence) with careful calibration; run QA evaluation checks, flag ambiguous examples, and suggest guideline clarifications to improve annotation guidelines compliance; contribute to RLHF workflows by producing high-signal comparisons and rationales that drive model performance improvement.

Project Types You May Support

LLM training pipelines (instruction following, tool-use, refusal calibration); RLHF preference datasets (pairwise comparisons, multi-turn conversations); prompt evaluation for helpfulness, harmlessness, and truthfulness; training data quality audits and inter-annotator agreement improvement; NLP datasets (classification, summarization, NER, sentiment, toxicity); computer vision annotation (retail, automotive, geospatial, medical imaging where permitted); content safety labeling for moderation and policy enforcement models.

Requirements

Mid-to-senior experience in data labeling, data annotation, QA evaluation, or trust & safety operations; strong English reading comprehension and careful attention to detail for rubric-based judgments; ability to follow annotation guidelines compliance standards and maintain consistency across long projects; comfort working with web-based labeling tools and spreadsheets; ability to document edge cases, escalate conflicts, and incorporate reviewer feedback; familiarity with NLP, computer vision annotation, or LLM evaluation concepts (preferred but not mandatory).

Quality and Performance Expectations

Maintain high training data quality through consistent labeling, low error rates, and strong calibration with project rubrics; support model performance improvement by producing clear rationales, accurate tags, and defensible evaluations; participate in QA evaluation cycles, rework tasks when needed, and adopt guideline updates quickly; handle sensitive content in content safety labeling with professionalism and policy adherence; protect confidential data and follow project security requirements.

Who This Role Is For

Professionals seeking full-time remote work who want to contribute to AI systems used by AI labs, tech startups, BPOs, and annotation vendors; candidates with experience in labeling operations, data QA, or evaluation; people interested in LLM training, RLHF, and prompt evaluation; detail-oriented reviewers who can translate natural language policies into consistent judgments.

How to Apply on Rex.zone

Apply through Rex.zone with your most recent resume and a short summary of your data labeling, QA evaluation, RLHF, or LLM evaluation experience. If you have prior work in named entity recognition, computer vision annotation, or content safety labeling, include project scope, tooling, and quality metrics (for example, audit scores or agreement rates).

Work Arrangement

Remote Type: Remote. Employment Type: FULL_TIME. This posting targets Miami-based candidates while remaining fully remote across the US where permitted by project requirements.

Frequently Asked Questions

Q: Are these remote data labeling jobs actually remote if I am in Miami?
Yes. The role is explicitly Remote. Miami is included as a location modifier to help local candidates find full-time remote opportunities through Rex.zone.
Q: What is data labeling in AI/ML training pipelines?
Data labeling (data annotation) is the process of adding structured tags, ratings, or boundaries to raw data such as text, images, audio, and conversations so models can learn patterns. In LLM training pipelines, it commonly includes prompt evaluation, response ranking, QA evaluation, and RLHF preference judgments to improve training data quality and model performance.
Q: Will I work on RLHF tasks?
Often, yes. Many projects include RLHF (Reinforcement Learning from Human Feedback) where you compare model responses, select the better answer, and provide rubric-based feedback that supports large language model evaluation and alignment.
Q: What domains can I expect: NLP, computer vision, or content safety labeling?
Projects may include NLP (classification, summarization, named entity recognition), computer vision annotation (bounding boxes, polygons, keypoints), and content safety labeling for moderation categories. Assignment depends on your skills, calibration results, and project availability.
Q: Is this role contract or freelance?
This posting is FULL_TIME. Rex.zone may also list contract and freelance roles separately; check Rex.zone for other employment types if you prefer contract work.
Q: Is this entry-level or senior?
This posting is Mid-Senior. However, Rex.zone may also offer entry-level and senior evaluation roles depending on the project and required accuracy thresholds.
Q: What tools will I use for annotation guidelines compliance and QA evaluation?
You will typically use web-based labeling tools, review dashboards, and spreadsheets. You will also follow written rubrics and decision trees to ensure annotation guidelines compliance and to pass QA evaluation audits.
Q: What skills should I highlight to be considered?
Highlight data labeling, data annotation, QA evaluation, training data quality, RLHF, LLM evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling, along with examples of consistent performance and guideline adherence.

230+Domains Covered

120K+PhD, Specialist, Experts Onboarded

50+Countries Represented

Industry-Leading Compensation

We believe exceptional intelligence deserves exceptional pay. Our platform consistently offers rates above the industry average, rewarding experts for their true value and real impact on frontier AI. Here, your expertise isn't just appreciated - it's properly compensated.

Work Remotely, Work Freely

No office. No commute. No constraints. Our fully remote workflow gives experts complete flexibility to work at their own pace, from any country, any time zone. You focus on meaningful tasks - we handle the rest.

Respect at the Core of Everything

AI trainers are the heart of our company. We treat every expert with trust, humanity, and genuine appreciation. From personalized support to transparent communication, we build long-term relationships rooted in respect and care.

Ready to Shape the Future of AI Data Operations?

Apply Now.