Senior Data Annotator Jobs in Madrid

Senior data annotator jobs in Madrid at Rex.zone focus on high-precision data labeling and evaluation workflows that improve large language model (LLM) training pipelines. You will apply annotation guidelines compliance, training data quality checks, and QA evaluation to deliver model performance improvement across NLP, computer vision, and content safety labeling tasks. This remote full-time role supports RLHF, prompt evaluation, and named entity recognition, partnering with AI labs, tech startups, and annotation vendors. Explore and apply through Rex.zone to work on production-grade datasets where accuracy, consistency, and auditability drive reliable AI systems.

Job Heading: Senior Data Annotator Jobs in Madrid

About the Role

Own end-to-end annotation and evaluation execution for multimodal datasets, ensuring training data quality, consistency, and clear rationale for ambiguous cases. You will label and review data for NLP and computer vision annotation, run QA evaluation, and contribute to RLHF and prompt evaluation loops that directly impact large language model evaluation and downstream model performance improvement.

What You Will Do

Deliver high-accuracy data labeling across tasks such as named entity recognition, intent/slot tagging, summarization quality ranking, and content safety labeling; perform peer review and adjudication for disagreement resolution; maintain annotation guidelines compliance by documenting edge cases and proposing guideline refinements; execute QA sampling plans, error analysis, and root-cause reporting to improve training data quality; support RLHF by ranking responses, writing preference rationales, and validating reward-model training sets; collaborate with project leads to track throughput, accuracy, and rework rates across LLM training pipelines.

Required Qualifications

Mid-Senior experience in data annotation, data labeling, or QA evaluation for ML datasets; strong command of annotation guidelines compliance, including interpreting ambiguous policies and maintaining consistency; demonstrated ability to perform error analysis and improve training data quality; familiarity with large language model evaluation concepts, including prompt evaluation and RLHF workflows; comfort working in remote, metrics-driven delivery environments.

Preferred Qualifications

Experience with named entity recognition, taxonomy design, and disagreement adjudication; exposure to computer vision annotation (bounding boxes, polygons, segmentation masks) and multimodal labeling; experience with content safety labeling and policy-based classification; prior work with AI labs, tech startups, BPOs, or annotation vendors; ability to translate evaluation findings into actionable recommendations for model performance improvement.

Quality Standards You Will Own

You will maintain measurable targets for training data quality, including accuracy, inter-annotator agreement, rework rate, and audit pass rates. You will enforce annotation guidelines compliance through calibration sessions, gold set verification, and documented decision logs that keep LLM training pipelines traceable and reproducible.

How Success Is Measured

Success is measured by consistent high-quality labels, lower ambiguity-driven error rates, strong QA evaluation outcomes, faster issue resolution, and improved signal quality for RLHF and prompt evaluation. Your work should contribute to model performance improvement by reducing noisy supervision and strengthening large language model evaluation reliability.

Who You Will Work With

You will collaborate with project managers, QA reviewers, and AI/ML stakeholders working across NLP, computer vision, and content safety labeling programs. The role may support multiple employer types including AI labs, tech startups, BPOs, and annotation vendors via Rex.zone project pipelines.

Apply on Rex.zone

Apply through Rex.zone to be considered for remote full-time senior data annotator jobs aligned to Madrid search intent. Ensure your application highlights data labeling depth, QA evaluation rigor, and experience improving training data quality for LLM training pipelines, RLHF, and prompt evaluation.

Frequently Asked Questions

Q: What does a Senior Data Annotator do in AI/ML?
A Senior Data Annotator produces and reviews high-quality labeled datasets used in LLM training pipelines, including data labeling, QA evaluation, and large language model evaluation. They also enforce annotation guidelines compliance, run calibration, adjudicate disagreements, and contribute to RLHF and prompt evaluation signals for model performance improvement.
Q: Is this role remote and full-time?
Yes. The job metadata specifies Remote Type: Remote and Employment Type: FULL_TIME.
Q: Why does the posting mention Madrid if the country is US?
The SEO keyword targets "senior data annotator jobs Madrid" while the job metadata keeps the provided default Country value unchanged (US). The role remains remote, which supports location-based search intent while maintaining the specified metadata.
Q: What annotation domains are included?
Typical domains include NLP labeling (named entity recognition, classification, ranking), computer vision annotation (bounding boxes, segmentation), content safety labeling, and LLM-specific tasks such as prompt evaluation and RLHF preference ranking.
Q: What skills are most important for this position?
Key skills include senior data annotation, data labeling, training data quality, annotation guidelines compliance, QA evaluation, RLHF evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, and understanding of LLM training pipelines.
Q: How is quality measured in data labeling work?
Quality is measured through accuracy on gold sets, inter-annotator agreement, audit pass rates, rework rates, and clear rationale documentation. Senior annotators also track error patterns and implement guideline updates to improve training data quality.
Q: What is RLHF and how does annotation support it?
RLHF (Reinforcement Learning from Human Feedback) uses human preference data to train reward models and improve LLM outputs. Annotation supports RLHF by ranking responses, writing preference rationales, validating prompts and completions, and ensuring QA evaluation standards so the preference signal is reliable.
Q: What types of employers use this work?
Employers commonly include AI labs, tech startups, BPOs, and annotation vendors that build datasets for NLP, computer vision, content safety labeling, and large language model evaluation programs.

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