Jobs at Data Annotation | Rex.zone

Jobs at Data Annotation is a recruiting page on Rex.zone for data annotation specialists, data labelers, QA evaluators, prompt evaluators, and RLHF contributors. These roles power AI/ML training workflows by creating and validating high-quality datasets across NLP, computer vision, and content safety. Candidates help improve training data quality, enforce annotation guidelines compliance, and drive model performance improvement through large language model evaluation and feedback. Opportunities include remote, contract, freelance, and full-time positions with entry-level and senior tracks at AI labs, tech startups, BPOs, and annotation vendors. Explore openings, submit your application, and build the next generation of LLM training pipelines with Rex.zone.

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About the Roles

Data annotation specialists convert raw data into model-ready labels and evaluations that enable robust AI systems. Work spans RLHF (Reinforcement Learning from Human Feedback), data labeling, QA evaluation, prompt evaluation, named entity recognition (NER), computer vision annotation, content safety labeling, and contributions to LLM training pipelines. The role emphasizes consistency, clear rationales, and measurable quality standards to maximize dataset reliability and downstream model performance.

Key Responsibilities

Produce accurate labels in text, image, audio, and video; validate outputs through structured QA evaluation; perform prompt evaluation and preference ranking for RLHF; execute named entity recognition, taxonomy mapping, and ontology alignment; apply computer vision annotation (bounding boxes, polygons, segmentation); enforce annotation guidelines compliance; generate rubrics, exemplars, and edge cases; document assumptions; and contribute feedback that supports training data quality and model performance improvement for large language model evaluation.

Required Skills

Strong attention to detail, ability to follow precise guidelines, and familiarity with AI/ML workflows; competence in NLP labeling (NER, sentiment, intent), computer vision annotation tools, and content safety frameworks; experience with LLM evaluation (pairwise ranking, critique, rubric scoring); clear written communication for rationales; comfort with productivity targets; basic understanding of model training pipelines; and proficiency with data platforms and issue tracking for transparent quality control.

Hiring Tracks and Domains

Openings across NLP (named entity recognition, sentiment, entity linking), computer vision (object detection, segmentation), content safety labeling (policy compliance, risk classification), LLM training pipelines (prompt ranking, preference collection, rubric-based critique), speech and ASR transcription, and multimodal datasets. Roles include entry-level, mid-level, and senior pathways, with skill-appropriate scopes and opportunities for specialization in QA evaluation and guidelines design.

Work Modalities and Employers

Rex.zone features remote, hybrid, and on-site roles offered as contract, freelance, and full-time engagements. Employers include AI labs, tech startups, BPOs, and annotation vendors. Candidates can filter jobs by seniority (entry-level, senior), domain focus (NLP, computer vision, content safety, LLM training), and geography. Flexible schedules are available for project-based work; dedicated teams support long-term full-time positions.

Application Process

Apply directly through Rex.zone by submitting a profile and resume. Selected candidates complete a short skills assessment and a sample annotation task to evaluate training data quality, annotation guidelines compliance, and consistency. Senior candidates may be asked to design QA checks, propose rubrics, and demonstrate large language model evaluation methods. Final steps include a brief interview and onboarding for tools and workflows.

Compensation and Benefits

Compensation varies by role type and seniority: competitive per-task rates for freelance and contract work; hourly or salaried packages for full-time roles; and performance bonuses tied to quality metrics and throughput. Senior contributors may receive premium rates for RLHF, guidelines creation, or QA leadership. Some employers offer health benefits, paid leave, and professional development budgets.

Tools and Platforms

Projects use industry-standard tools for annotation and evaluation, including platforms for NLP labeling, computer vision (bounding boxes, segmentation), and content safety workflows. Candidates may work with Label Studio, CVAT, SuperAnnotate, Prodigy, internal LLM evaluation dashboards, and issue-tracking systems to manage quality, throughput, and audit trails across the data lifecycle.

Why Rex.zone

Rex.zone connects skilled annotators with vetted employers, streamlines application and onboarding, and provides clear visibility into domain focus, modality, and seniority. The platform emphasizes training data quality, transparent guidelines, and measurable outcomes, helping candidates grow from entry-level to senior roles while contributing directly to model performance improvement in real-world AI systems.

Frequently Asked Questions

  • Q: What is a data annotation specialist?

    A data annotation specialist labels and evaluates datasets for AI/ML training. Responsibilities include structured QA evaluation, prompt evaluation for RLHF, named entity recognition, computer vision annotation, and content safety labeling to support high-quality model development.

  • Q: Do you offer remote and freelance roles?

    Yes. Rex.zone lists remote, contract, freelance, and full-time roles across entry-level and senior tracks. You can filter jobs by modality, domain (NLP, computer vision, content safety, LLM training), and employer type (AI labs, tech startups, BPOs, annotation vendors).

  • Q: How are candidates evaluated during hiring?

    Most employers require a short skills assessment and a sample labeling task. Reviewers check training data quality, annotation guidelines compliance, rationale clarity, throughput, and consistency. Senior candidates may demonstrate rubric design and large language model evaluation approaches.

  • Q: Is prior experience with RLHF required?

    Not always. Entry-level roles often provide training for prompt evaluation and pairwise preference tasks. Senior roles typically expect familiarity with RLHF workflows, critique generation, and quality safeguards that improve model performance.

  • Q: What tools should I know?

    Experience with common annotation tools (e.g., Label Studio, CVAT, SuperAnnotate, Prodigy) is helpful. Comfort with internal dashboards for LLM evaluation, issue tracking, and version control will improve your productivity and quality outcomes.

  • Q: How is compensation determined?

    Compensation depends on role type, domain complexity, and seniority. Freelance and contract roles often pay per task or hour, while full-time positions offer salaries. Quality metrics, adherence to guidelines, and throughput can unlock bonuses or higher tiers.

  • Q: Can I apply without a technical degree?

    Yes. Many entry-level roles focus on attention to detail and following instructions. A basic understanding of AI/ML workflows and strong written communication are valuable. Rex.zone provides resources to help you ramp quickly.

  • Q: What domains are in highest demand?

    NLP labeling (NER, sentiment, intent), computer vision annotation (detection, segmentation), content safety labeling, and LLM training pipelines (prompt ranking, preference collection, critique) are consistently in demand across employers on Rex.zone.

230+Domains Covered
120K+PhD, Specialist, Experts Onboarded
50+Countries Represented

Industry-Leading Compensation

We believe exceptional intelligence deserves exceptional pay. Our platform consistently offers rates above the industry average, rewarding experts for their true value and real impact on frontier AI. Here, your expertise isn't just appreciated—it's properly compensated.

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