Remote Data Labeling Jobs Paris

Rex.zone is hiring for remote data labeling jobs aligned with Paris talent, focused on training-data creation for AI/ML systems. You will label text, images, audio, and video to support large language model evaluation, RLHF (Reinforcement Learning from Human Feedback), prompt evaluation, and content safety labeling. Your work improves training data quality, annotation guidelines compliance, and model performance improvement across NLP and computer vision annotation workflows. This is a full-time remote role supporting AI labs, tech startups, and annotation vendors that rely on high-precision QA evaluation and scalable LLM training pipelines. Apply via Rex.zone to join a production-grade data annotation operation.

Job Heading: Remote Data Labeling Jobs Paris

About the Role

As a remote data labeling specialist aligned to Paris hiring needs, you will produce high-quality labeled datasets used to train, evaluate, and monitor AI systems. You will follow annotation guidelines, apply consistent labels, document edge cases, and contribute to calibration sessions to improve inter-annotator agreement. Work includes NLP labeling (classification, sentiment, intent, named entity recognition), RLHF ranking and preference labeling, prompt evaluation for helpfulness/harmlessness, and computer vision annotation such as bounding boxes, polygons, keypoints, and segmentation masks. You will collaborate with QA to ensure training data quality and help close the loop between labeling decisions and model performance improvement.

What You Will Do

Key responsibilities include: (1) Perform data annotation across text, image, audio, and video tasks using production labeling tools, (2) Execute RLHF workflows including pairwise preference ranking, justification tagging, and rubric-based scoring for large language model evaluation, (3) Conduct prompt evaluation and response grading for accuracy, instruction-following, and safety, (4) Apply content safety labeling policies for toxicity, self-harm, hate, and sensitive attributes, (5) Maintain annotation guidelines compliance and escalate ambiguous cases with clear examples, (6) Participate in QA evaluation, audits, and disagreement resolution to improve training data quality, (7) Track labeling throughput and accuracy metrics while meeting full-time delivery goals, (8) Provide feedback to improve rubrics, taxonomies, and labeling ontology for scalable LLM training pipelines.

Required Qualifications

You should have: (1) Experience in data labeling or data annotation operations (text and/or computer vision annotation), (2) Familiarity with NLP tasks such as named entity recognition, intent classification, or semantic similarity, (3) Ability to learn and apply strict rubrics for RLHF and QA evaluation, (4) Strong attention to detail and consistency under changing guidelines, (5) Comfort handling sensitive content in content safety labeling workflows, (6) Clear written communication for edge-case documentation and reviewer notes, (7) Reliable internet and availability for full-time remote work.

Preferred Qualifications

Nice to have: (1) Hands-on experience with large language model evaluation, prompt evaluation, or rubric design, (2) Experience with computer vision annotation (bounding boxes, polygons, segmentation), (3) Knowledge of dataset curation, sampling strategies, and bias analysis, (4) Prior work with annotation vendors, BPOs, or AI labs, (5) Familiarity with QA frameworks such as spot checks, gold sets, and inter-annotator agreement, (6) Comfort with basic scripting or spreadsheet-based analysis for error categorization.

Tools and Workflows

You may work with labeling platforms and internal tools to: manage queues, follow versioned guidelines, complete calibration tasks, handle gold-standard evaluations, and submit QA notes. Workflows include: task briefing, pilot labeling, reviewer feedback loops, disagreement adjudication, and continuous guideline refinement to reduce error modes that affect model performance improvement.

Remote Work Notes

This role is Remote and remains explicitly remote. Although this page targets remote data labeling jobs for Paris recruiting needs, the role is managed under a US-based posting entity. You will collaborate asynchronously with distributed teams supporting NLP, computer vision annotation, RLHF, and content safety labeling programs.

Compensation

Salary Range (USD, per year): 63360 to 126720, depending on relevant annotation experience, QA evaluation performance, domain fit (NLP, computer vision, content safety), and demonstrated ability to follow annotation guidelines compliance at scale.

How to Apply

Apply through Rex.zone and select the Remote Data Labeling Jobs Paris listing. Include a brief summary of your annotation background (data labeling, RLHF, prompt evaluation, QA evaluation, named entity recognition, or computer vision annotation) and any prior experience improving training data quality for LLM training pipelines.

Frequently Asked Questions

Q: What does a remote data labeling job involve?
Remote data labeling involves applying consistent labels to text, images, audio, or video so AI/ML models can learn from structured examples. Work commonly includes NLP tagging (classification, named entity recognition), computer vision annotation (bounding boxes, segmentation), and QA evaluation to maintain training data quality.
Q: Do these remote data labeling jobs include RLHF tasks?
Yes. Depending on the project, you may perform RLHF workflows such as pairwise preference ranking, rubric-based scoring, and justification tagging used in large language model evaluation and model performance improvement.
Q: Is this role full-time and remote?
Yes. Employment Type is FULL_TIME and Remote Type is Remote. The work is performed remotely with distributed project teams.
Q: Why does the page mention Paris if the country is US?
The page is designed for Paris-aligned recruiting needs while the posting entity and metadata country field remain US as specified. The position remains Remote and supports global labeling programs through Rex.zone.
Q: What skills are most important for success?
High attention to detail, annotation guidelines compliance, consistency across edge cases, clear written communication, and comfort with QA evaluation. Familiarity with RLHF, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling is strongly relevant.
Q: What types of employers use this kind of work?
AI labs, tech startups, BPOs, and annotation vendors commonly rely on remote data labeling teams to produce datasets for NLP, computer vision annotation, content safety labeling, and LLM training pipelines.

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