Data annotation freelance jobs are project-based roles for data labeling specialists who support LLM training pipelines and RLHF. At Rex.zone, freelancers handle prompt evaluation, QA evaluation, named entity recognition for NLP, computer vision annotation for images/videos, and content safety labeling to drive model performance improvement and training data quality under annotation guidelines compliance. These roles plug directly into AI/ML workflows—from dataset creation to large language model evaluation—ensuring reliable inputs for supervised fine-tuning and reinforcement learning from human feedback. Explore remote, contract, and flexible engagements across domains on Rex.zone and start contributing to high-impact machine learning systems.
As a freelance data annotator on Rex.zone, you will label, categorize, and evaluate datasets used in AI model training. Work spans NLP (NER, sentiment, intent), computer vision annotation (bounding boxes, segmentation, keypoints), content safety labeling, prompt evaluation for LLMs, and QA evaluation of outputs. You will follow detailed taxonomies, maintain high inter-annotator agreement, and uphold training data quality through strict annotation guidelines compliance.
Execute precise data labeling tasks with high accuracy; audit and refine guidelines; perform adversarial and edge-case reviews; conduct large language model evaluation and RLHF preference ranking; track issues and feedback loops to enable model performance improvement; collaborate with QA leads and PMs to meet throughput, quality assurance, and SLA targets.
Strong attention to detail; familiarity with ontology design, annotation taxonomies, and quality rubrics; experience with platforms such as Labelbox, Scale, SuperAnnotate, Diffgram, or custom tools; comfort with spreadsheets, JSON, and simple scripts (regex, Python basics) for data hygiene; understanding of NLP pipelines, computer vision datasets, content safety policies, and LLM training workflows.
Roles include remote, contract, freelance, and full-time opportunities across NLP, computer vision, speech, content safety, and LLM training. Openings range from entry-level to senior annotators, quality reviewers, and labeling leads. Projects may include prompt evaluation for chat models, NER for healthcare and finance, CV tasks for autonomous systems, and safety labeling for generative content.
AI labs, tech startups, BPOs, and annotation vendors source talent via Rex.zone, alongside research institutes and enterprise teams in e-commerce, fintech, healthcare, and media. Workstreams connect to real-world AI/ML pipelines—data collection, curation, labeling, QA evaluation, and continuous improvement cycles for supervised fine-tuning and RLHF.
Pay varies by domain and complexity: per-task, per-hour, or milestone-based contracts. Specialized work (content safety, medical NER, CV segmentation) often commands higher rates. Senior reviewers and leads may receive premium compensation for guideline creation, training data quality audits, and annotation guideline compliance oversight.
Create your profile, showcase domain experience, and attach sample annotations or portfolios. Browse active postings, filter by remote, contract, freelance, full-time, entry-level, or senior roles, then submit tailored applications. Rex.zone centralizes discovery, communication, and onboarding for AI labs, startups, BPOs, and vendors.
Data annotation is the structured labeling and evaluation of text, images, audio, and video used in AI training. For LLMs it includes prompt evaluation, preference ranking for RLHF, named entity recognition, content safety labeling, and QA evaluation to ensure reliable datasets and model performance improvement.
Common domains are NLP (NER, sentiment, intent), computer vision annotation (bounding boxes, segmentation), speech/transcription, and safety labeling. Many projects involve large language model evaluation and reinforcement learning from human feedback.
Entry-level roles exist, but familiarity with annotation guidelines, quality rubrics, and basic tooling is helpful. Senior roles require demonstrated expertise, domain knowledge, and experience maintaining training data quality and annotation guidelines compliance.
Quality is tracked with inter-annotator agreement, audit sampling, consistency checks, and reviewer feedback. Teams emphasize guideline clarity, coverage of edge cases, and alignment with task-specific quality metrics to support trustworthy AI training pipelines.
Popular tools include Labelbox, SuperAnnotate, Scale, and internal platforms. Comfort with spreadsheets, JSON, and simple scripting is helpful for QA evaluation and data hygiene. You’ll also use task management tools for throughput and issue tracking.
Yes. Most roles are remote and offered as contract or freelance. Full-time options exist with AI labs, tech startups, BPOs, and annotation vendors. You can filter by remote, contract, freelance, full-time, entry-level, or senior on Rex.zone.
Create a Rex.zone profile, list domain strengths, upload sample annotations, and apply to relevant postings. Follow the brief precisely, complete trial tasks, and demonstrate consistent accuracy to qualify for ongoing, higher-paying projects.
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