Remote Data Annotation Jobs in Lisbon

Remote data annotation jobs in Lisbon focus on producing high-quality labeled datasets that power real AI/ML training workflows on Rex.zone, including LLM instruction tuning, RLHF preference ranking, data labeling for NLP and computer vision, prompt evaluation, and QA evaluation. You will follow annotation guidelines compliance, improve training data quality, and support model performance improvement through consistent labeling decisions, edge-case handling, and content safety labeling. This full-time remote role supports AI labs, tech startups, and annotation vendors that rely on accurate annotations to strengthen large language model evaluation and production-ready ML systems.

Job Heading: Remote Data Annotation Jobs in Lisbon

About the Role

You will perform remote data annotation work aligned to Lisbon-targeted recruiting needs while operating in globally distributed AI data operations. Your day-to-day includes labeling text, images, and structured items; performing LLM evaluation tasks such as preference ranking and response quality scoring; and applying RLHF-style rubrics to improve large language model behavior. You will contribute to training data pipelines by resolving ambiguity, documenting rationale for hard cases, and maintaining consistent application of annotation guidelines.

What You Will Do

Core workflows include: creating and validating labeled datasets for NLP tasks (classification, summarization, sentiment, named entity recognition); computer vision annotation (bounding boxes, polygons, keypoints, segmentation masks); prompt evaluation and response grading for instruction-following and safety; QA evaluation through sampling, disagreement analysis, and audit trails; and partnering with operations to refine rubrics, edge-case playbooks, and quality gates that protect training data quality and downstream model performance improvement.

Tools, Data Types, and Evaluation Methods

You will work with web-based annotation platforms and structured task queues, handling datasets such as chat transcripts, prompts, policy documents, product text, and images. Evaluation methods may include rubric-based scoring, pairwise ranking for RLHF, error taxonomy tagging, inter-annotator agreement checks, and gold-set calibration. You will be expected to follow versioned guidelines, track changes, and keep annotations consistent across shifts and projects.

Required Qualifications

Mid-to-senior experience in data annotation, data labeling, or AI/ML evaluation; proven ability to follow detailed annotation guidelines and handle ambiguous cases; strong written communication for rationale notes and QA feedback; familiarity with LLM evaluation concepts, prompt evaluation, and/or RLHF-style preference ranking; and a quality-first mindset with comfort working in remote, metric-driven production environments.

Preferred Qualifications

Experience with named entity recognition, taxonomy design, or content moderation and content safety labeling; exposure to computer vision annotation (bounding boxes, segmentation); understanding of dataset bias, leakage, and label noise; experience performing audit sampling, disagreement adjudication, and quality measurement (precision/recall style thinking, agreement metrics); and experience supporting AI labs, tech startups, BPOs, or annotation vendors.

Quality Standards and Success Metrics

Success is measured by annotation guidelines compliance, training data quality, throughput balanced with accuracy, and consistent decision-making under ambiguity. You will be evaluated on calibration performance against gold tasks, low rework rates, clear documentation for edge cases, and contributions to improved rubrics that reduce disagreement and increase model performance improvement in downstream evaluations.

Remote Work Details

This is a full-time Remote role with asynchronous task execution and scheduled calibration sessions. You will coordinate with distributed reviewers, QA evaluators, and project leads, ensuring consistent labeling across time zones. Even though this page targets Lisbon recruiting demand, the job is explicitly Remote and supports global project staffing via Rex.zone.

How to Apply on Rex.zone

Apply through Rex.zone by submitting your profile and completing any required qualification tasks. Be prepared to demonstrate careful guideline reading, consistent labeling decisions, and clear written rationale for edge cases. Applicants who show strong QA evaluation habits and reliable rubric adherence are prioritized for long-running LLM training pipeline work.

Frequently Asked Questions

Q: What are remote data annotation jobs in Lisbon?
They are Remote roles recruited with Lisbon intent where you label and evaluate training data used in AI/ML systems, including data labeling for NLP and computer vision, RLHF preference ranking, prompt evaluation, and QA evaluation tasks delivered through Rex.zone.
Q: Is this role truly Remote?
Yes. Remote Type is Remote and the role is designed for distributed delivery, with online annotation tools, calibration sessions, and QA workflows managed remotely.
Q: What skills are most important for this job?
Data annotation, data labeling, RLHF-style evaluation, LLM evaluation, prompt evaluation, QA evaluation, annotation guidelines compliance, training data quality awareness, named entity recognition, computer vision annotation fundamentals, and content safety labeling practices.
Q: What kinds of tasks will I do day-to-day?
You will label text and images, apply rubrics to grade LLM outputs, perform preference ranking for RLHF, complete QA checks, document edge cases, and contribute to improving annotation guidelines for consistent training data quality.
Q: Which AI domains does this role support?
Common domains include NLP, large language model evaluation, content safety labeling, and computer vision annotation, depending on the project mix from AI labs, tech startups, BPOs, and annotation vendors.
Q: What employment type and experience level is expected?
Employment Type is FULL_TIME and Experience Level is Mid-Senior, reflecting an expectation of reliable independent execution, calibration discipline, and strong QA habits.
Q: How does this work connect to real AI/ML training pipelines?
Your labels and evaluations become supervised fine-tuning data and RLHF signals, influence training data quality, reduce label noise, and directly support model performance improvement and large language model evaluation outcomes.
Q: What is the compensation range listed on this page?
Salary Currency is USD with Salary Min 63360 and Salary Max 126720, Pay Period YEAR, aligned to the metadata for this posting.

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