Remote Data Annotation Jobs Milan

Remote Data Annotation Jobs Milan at Rex.zone connect Milan-based talent with full-time, remote AI data work that powers LLM training pipelines, RLHF, and model evaluation. You will label and review training data, follow annotation guidelines compliance, and perform QA evaluation to improve model performance across NLP, computer vision, and content safety. Typical workflows include data labeling for instruction tuning, prompt evaluation for large language model evaluation, and named entity recognition with rigorous training data quality checks. Explore Rex.zone to apply, match to active projects from AI labs, tech startups, and annotation vendors, and contribute to measurable model performance improvement through consistent, high-accuracy annotations.

Job Heading: Remote Data Annotation Specialist (Milan)

About the Role

As a Remote Data Annotation Specialist aligned to Milan hiring demand, you will create and evaluate high-quality training datasets for machine learning systems. You will annotate text, images, and multi-turn prompts; validate edge cases; and apply consistent labeling decisions to support RLHF, instruction tuning, and automated evaluation. Your work directly impacts training data quality, reduces label noise, and improves downstream model behavior and safety.

What You Will Do

You will produce labeled examples for NLP and computer vision annotation tasks; perform prompt evaluation and response ranking for large language model evaluation; execute QA evaluation using gold sets, peer review, and disagreement resolution; apply named entity recognition and taxonomy-based labeling; support content safety labeling (policy-driven moderation categories); document annotation guidelines compliance and escalate ambiguous cases; and partner with data ops leads to track error patterns that affect model performance improvement.

Core Workflows You Will Support

RLHF ranking and preference labeling for assistant responses; instruction-following grading and rubric-based scoring; training data quality audits, including inter-annotator agreement checks; prompt evaluation with adversarial and edge-case prompts; named entity recognition and span labeling; computer vision annotation such as bounding boxes, polygons, and keypoints; and content safety labeling for toxicity, self-harm, and regulated content categories.

Requirements

3+ years of experience in data annotation, data labeling, QA evaluation, or ML data operations; strong written English and the ability to follow detailed rubrics; familiarity with LLM evaluation concepts such as preference ranking, calibration, and instruction tuning; comfort working with annotation tools and structured labeling schemas; ability to maintain high accuracy under throughput targets; and a security-minded approach to handling sensitive content in content safety labeling workflows.

Preferred Qualifications

Experience with RLHF pipelines and disagreement adjudication; prior work in named entity recognition, document understanding, or knowledge extraction; exposure to computer vision annotation (bounding boxes, segmentation); experience building or maintaining annotation guidelines; familiarity with sampling, gold set creation, and training data quality metrics; and comfort collaborating with distributed teams across AI labs, tech startups, BPOs, and annotation vendors.

Tools and Quality Standards

You will work in structured annotation platforms with versioned rubrics, task queues, and audit trails. Quality expectations include annotation guidelines compliance, consistent label application, documented rationales for subjective judgments, and measurable improvements in QA evaluation results over time. You will use feedback loops, error taxonomies, and calibrated review processes to ensure reliable large language model evaluation outcomes.

Employment Notes

This is a FULL_TIME Remote role. Rex.zone supports remote work expectations including defined schedules, secure access practices, and quality reporting. Candidates aligned with Milan-based availability are encouraged to apply; projects may support multiple time zones depending on client needs.

How to Apply on Rex.zone

Apply through Rex.zone by submitting your resume and highlighting relevant data annotation, RLHF, LLM evaluation, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling experience. Include examples of guideline-driven work, quality metrics, and any rubric-based evaluation projects.

Frequently Asked Questions

Q: What does “Remote Data Annotation Jobs Milan” mean on Rex.zone?
It refers to remote, full-time data annotation roles marketed to Milan-based candidates while the employer country is listed as US. You work remotely on AI training data tasks such as data labeling, RLHF preference ranking, prompt evaluation, and QA evaluation, delivered through Rex.zone.
Q: Is this job truly Remote?
Yes. The role is explicitly marked Remote and is designed for remote execution using online annotation tools and QA workflows.
Q: What types of annotation will I do?
Typical tasks include data labeling for NLP and LLM training pipelines, named entity recognition, prompt evaluation and rubric scoring, RLHF response ranking, computer vision annotation, and content safety labeling with policy-based categories.
Q: How is quality measured?
Quality is measured using training data quality checks such as gold set accuracy, peer review outcomes, inter-annotator agreement, audit pass rates, and consistency with annotation guidelines compliance, all tied to model performance improvement goals.
Q: What skills should I highlight to match this posting?
Highlight data annotation, data labeling, RLHF, LLM evaluation, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, and annotation guidelines compliance, plus evidence you can maintain accuracy and consistency under throughput targets.
Q: Are there other work arrangements besides full-time?
This posting is FULL_TIME by default. Rex.zone may also host contract or freelance annotation roles on other pages, but this specific job metadata is full-time and remote.
Q: What kinds of employers use Rex.zone for these roles?
Projects commonly come from AI labs, tech startups, BPOs, and specialized annotation vendors that need scalable training data production, evaluation, and QA for NLP, computer vision, and content safety domains.
Q: Does this role involve sensitive content?
It can. Content safety labeling and moderation-style evaluation may include exposure to sensitive categories. Clear policies, rubrics, and support processes are used to ensure consistent and safe handling.

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