Remote Data Labeling Jobs Milan

Remote data labeling jobs in Milan at Rex.zone focus on producing high-quality training data for AI systems through data labeling, RLHF evaluation, and QA review. You will annotate text, images, audio, and video to improve large language model evaluation, computer vision annotation accuracy, and content safety labeling outcomes. This full-time remote role supports real-world LLM training pipelines with consistent annotation guidelines compliance, prompt evaluation, named entity recognition, and training data quality checks. Explore Rex.zone opportunities to help drive model performance improvement for AI labs, tech startups, and annotation vendors serving enterprise ML teams.

Job Heading: Remote Data Labeling Jobs Milan

About the Role

As a Remote Data Labeling professional supporting Milan-aligned hiring demand, you will create and verify labeled datasets used to train and evaluate machine learning models. Your work will span LLM evaluation and prompt evaluation, RLHF preference ranking, and multi-modal annotation (NLP, computer vision annotation, and content safety labeling). You will apply detailed annotation guidelines, resolve edge cases, document decisions, and collaborate with QA reviewers to ensure consistent training data quality that supports measurable model performance improvement.

What You Will Do

You will label and review training data across text, image, audio, and video tasks; perform RLHF comparisons and preference judgments for assistant responses; execute QA evaluation using rubrics for accuracy, relevance, and safety; follow annotation guidelines compliance and update notes when policies change; run named entity recognition and taxonomy tagging for structured datasets; complete prompt evaluation workflows to detect hallucinations, instruction-following gaps, and policy violations; support content safety labeling for sensitive categories; report dataset issues, ambiguities, and failure patterns that impact large language model evaluation.

Core Workflows You Will Support

Large language model evaluation with scoring rubrics; RLHF ranking and pairwise preference data creation; training data quality audits and inter-annotator agreement checks; computer vision annotation including bounding boxes, polygons, segmentation masks, and attribute tagging; NLP labeling including NER, intent classification, sentiment, and topic taxonomies; content safety labeling for harmful or restricted content; QA evaluation loops that feed back into guideline updates and model performance improvement tracking.

Required Qualifications

Mid-Senior experience in data labeling, data annotation, or ML QA evaluation; strong ability to interpret annotation guidelines and maintain annotation guidelines compliance; proven attention to detail and comfort with repetitive quality-focused workflows; familiarity with LLM evaluation concepts such as instruction-following, groundedness, and safety; ability to write clear rationale notes for edge cases; experience working independently in a remote full-time environment with consistent throughput and quality targets.

Preferred Qualifications

Hands-on exposure to RLHF pipelines, preference ranking, or prompt evaluation; experience with named entity recognition or ontology design; familiarity with computer vision annotation tools and segmentation workflows; experience in content safety labeling and policy-based QA evaluation; comfort analyzing disagreements and improving training data quality via structured feedback; prior work with AI labs, tech startups, BPOs, or annotation vendors supporting production ML teams.

Quality Standards and Metrics

You will be measured on training data quality, annotation guidelines compliance, QA evaluation pass rates, consistency across annotators, and turnaround time. You will also contribute to model performance improvement by flagging systematic errors, ambiguous prompts, and rubric gaps that affect large language model evaluation and downstream training outcomes.

Tools and Collaboration

You will work in remote annotation platforms and QA dashboards, follow versioned guidelines, and coordinate with project leads on sampling plans and escalation paths. Collaboration includes clarification of edge cases, calibration sessions to align on rubrics, and documentation that improves future labeling consistency across NLP, computer vision annotation, and content safety labeling queues.

Employment Details

Remote Type: Remote; Employment Type: FULL_TIME; Experience Level: Mid-Senior; Country: US. This posting targets candidates searching for remote data labeling jobs in Milan while remaining a fully remote role through Rex.zone.

How to Apply

Apply via Rex.zone and be ready to complete an online evaluation covering annotation guidelines compliance, QA evaluation judgment, and sample tasks spanning LLM evaluation, RLHF ranking, named entity recognition, and content safety labeling. Qualified candidates may be invited to calibration tasks that validate training data quality and consistency expectations.

Frequently Asked Questions

Q: Are these remote data labeling jobs in Milan fully remote?
Yes. The role is explicitly Remote and can be performed from Milan or other locations, while the job metadata remains listed under Country: US and Company: Rex.zone.
Q: What types of data labeling tasks will I do?
Tasks typically include LLM evaluation, RLHF preference ranking, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling, plus QA evaluation to ensure training data quality.
Q: Is this a full-time role or contract/freelance?
This posting is for FULL_TIME employment. Rex.zone may also host contract or freelance tasks, but this specific job is full-time.
Q: What experience level is required?
The role is Mid-Senior and expects prior experience with data labeling, annotation guidelines compliance, and quality-focused review work in AI/ML training pipelines.
Q: How does my work impact AI model performance?
High-quality labeled data and consistent QA evaluation improve training data quality, reduce noise in RLHF signals, strengthen large language model evaluation benchmarks, and contribute directly to model performance improvement.
Q: Do I need a background in NLP or computer vision?
It is helpful but not always required. Many projects include both NLP and computer vision annotation components; strong guideline adherence, QA evaluation judgment, and careful reasoning are essential.
Q: What is included in the hiring assessment?
Assessments commonly test annotation guidelines compliance, edge-case reasoning, QA evaluation accuracy, and practical tasks such as RLHF ranking, prompt evaluation, and basic tagging like named entity recognition.
Q: What salary range is listed for this role?
The listed range is USD 63360 to USD 126720 per YEAR, aligned to the provided job metadata.

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