Remote Data Annotation Jobs in Rome

Remote data annotation jobs in Rome at Rex.zone focus on creating and evaluating high-quality training data for AI systems. In this full-time remote role, you will label text, images, audio, and video; apply annotation guidelines; and perform QA evaluation to improve model performance across NLP, computer vision, and LLM training pipelines. You may support RLHF workflows, prompt evaluation, content safety labeling, and named entity recognition to help AI labs, tech startups, and annotation vendors ship safer and more accurate models. Explore and apply through Rex.zone to join production-grade data labeling operations with measurable impact on training data quality and large language model evaluation.

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About the Role

You will deliver consistent, policy-compliant annotations used to train and evaluate ML models. Work includes text classification, entity extraction, summarization checks, prompt/response preference ranking for RLHF, and content safety labeling. You will follow detailed annotation guidelines, log edge cases, and collaborate asynchronously with QA reviewers to improve training data quality and reduce label noise in LLM training pipelines.

Key Responsibilities

Tasks are designed to improve model performance improvement through reliable labels and evaluations. Typical responsibilities include: (1) Data labeling across NLP and computer vision annotation tasks (bounding boxes, segmentation, OCR verification where applicable), (2) Named entity recognition and relation extraction, (3) Prompt evaluation and response ranking for large language model evaluation and RLHF, (4) QA evaluation using rubrics, inter-annotator agreement checks, and audit samples, (5) Content safety labeling for policy categories (harassment, self-harm, adult content, misinformation) where required, (6) Annotation guidelines compliance, issue escalation, and documenting ambiguous cases.

Required Qualifications

Mid-senior experience in data annotation or QA evaluation for AI/ML projects. You can interpret complex rubrics, maintain high consistency, and communicate labeling rationale clearly. Familiarity with LLM evaluation concepts, RLHF-style preference labeling, and structured labeling for NLP (NER) or CV tasks is expected. You can work independently in a remote environment with strong attention to detail and time management.

Preferred Qualifications

Experience with annotation vendors, BPO operations, AI labs, or tech startups delivering production datasets. Prior work with prompt evaluation, content safety labeling, multilingual NLP, or computer vision annotation at scale. Comfort using annotation tools, QA sampling plans, and metrics such as precision/recall proxies, agreement rates, and guideline drift tracking.

Work Model and Collaboration

This is a full-time Remote role supporting distributed teams. You will receive project-specific annotation guidelines, calibration examples, and QA feedback loops. Collaboration is asynchronous with documented decisions to keep annotation guidelines compliance consistent across contributors.

How to Apply

Apply via Rex.zone with a brief summary of your data labeling and QA evaluation experience. Include examples of task types you have completed (NER, classification, RLHF preference ranking, computer vision annotation, content safety labeling) and the quality methods you used to maintain training data quality.

Frequently Asked Questions

Q: Are these remote data annotation jobs in Rome fully remote?
Yes. The Remote Type is Remote, and work is performed remotely with asynchronous collaboration through Rex.zone workflows.
Q: What kind of data will I annotate?
You may label text, images, audio, or video depending on project needs, including NLP tasks (classification, named entity recognition), computer vision annotation, and content safety labeling.
Q: Do these jobs include RLHF or LLM evaluation work?
Many projects include RLHF-style preference labeling, prompt evaluation, and large language model evaluation to improve model behavior and reduce failure modes.
Q: Is this role contract, freelance, or full-time?
This posting is FULL_TIME. Rex.zone may host other modifier types (contract or freelance) on the platform, but this role remains full-time.
Q: What experience level is required?
The Experience Level is Mid-Senior, emphasizing consistent annotation guidelines compliance, QA evaluation rigor, and the ability to handle complex edge cases.
Q: What skills are most important to succeed?
Core skills include data annotation, data labeling, QA evaluation, RLHF, LLM evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, and strong adherence to annotation guidelines.
Q: How does QA work for data labeling?
QA evaluation typically includes calibration tasks, audit sampling, rubric-based reviews, inter-annotator agreement checks, and feedback loops that drive training data quality and model performance improvement.
Q: Who typically hires for this type of work?
Employers commonly include AI labs, tech startups, BPOs, and annotation vendors building datasets for NLP, computer vision, and LLM training pipelines.

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