AI Data Annotation Jobs

AI data annotation jobs are the core entity powering AI/ML training pipelines on Rex.zone. Annotators and RLHF raters label text, images, audio, and code; evaluate prompts and model outputs; ensure annotation guidelines compliance; and drive training data quality for model performance improvement and large language model evaluation. Roles span data labeling, QA evaluation, named entity recognition (NER), computer vision annotation, content safety labeling, and prompt evaluation. Apply for remote, contract, freelance, full-time, entry-level, and senior positions with AI labs, tech startups, BPOs, and annotation vendors. Join Rex.zone to build reliable datasets, strengthen LLM alignment, and accelerate deployment-ready AI.

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Key Responsibilities

• Execute high-quality data labeling across NLP, computer vision, and multimodal datasets. • Conduct RLHF tasks: preference ranking, pairwise comparison, and prompt evaluation for LLM alignment. • Perform QA evaluation and audits to ensure annotation guidelines compliance and training data quality. • Tag entities for NER, annotate bounding boxes/polygons for CV, and classify content safety categories. • Review model outputs for accuracy, toxicity, bias, and hallucination; document edge cases. • Collaborate on annotation guideline refinement and contribute to model performance improvement. • Use Rex.zone tooling to track productivity, quality scores, and feedback cycles.

Required Qualifications

• Detail-oriented with strong reading comprehension and consistency. • Familiarity with ML concepts: datasets, labeling schemas, LLM evaluation, and RLHF. • Experience with annotation tools (text tagging, image bounding boxes, audio transcription). • Ability to follow rigorous SOPs and meet throughput/quality targets. • Clear written communication; fluency in English (additional languages a plus). • Privacy-aware mindset and adherence to content safety practices.

Preferred Experience

• Prior work in data labeling, human-in-the-loop ML, or annotation vendors/BPOs. • Domain exposure in NLP (NER, sentiment, intent), computer vision (detection, segmentation), and content safety. • Experience evaluating LLMs, prompts, and generative outputs for alignment and risk. • Background in linguistics, psychology, data operations, or applied ML. • Familiarity with taxonomy design, rubric creation, and inter-annotator agreement (IAA).

Tools & Platforms

• Rex.zone job marketplace and quality dashboards. • Annotation suites for text, CV, and audio; prompt evaluation workflows; RLHF rater interfaces. • Issue tracking, guideline repositories, and reviewer workflows. • Secure environments for sensitive content with audit trails and access controls.

Domains We Hire For

• NLP: named entity recognition, sentiment analysis, intent classification, text summarization. • Computer Vision: bounding boxes, polygons, segmentation, OCR. • Content Safety: toxicity, hate, self-harm, sexual content, political misinformation. • LLM Training: prompt evaluation, preference ranking, chain-of-thought checks, hallucination detection.

Employment Types & Locations

• Remote, hybrid, and on-site opportunities globally. • Full-time, part-time, contract, freelance, temporary, and internships. • Roles across AI labs, tech startups, BPOs, and specialized annotation vendors.

Impact & Growth

• Improve model performance through better training data quality and robust QA. • Learn advanced ML workflows, contribute to LLM alignment via RLHF, and shape safety taxonomies. • Progress from entry-level annotator to senior reviewer, QA lead, project manager, or data operations specialist.

How to Apply

Create a profile on Rex.zone, select AI data annotation jobs, verify language/domain skills, and complete sample tasks. Qualified candidates are matched to projects by domain (NLP, CV, content safety, LLM training) and employment type (full-time, contract, freelance).

Role-specific Q&A

  • Q: What is RLHF and how does it relate to annotation?

    RLHF (Reinforcement Learning from Human Feedback) uses human preference ratings and prompt evaluations to align LLM behavior. Annotators act as raters to compare outputs, rank responses, and give feedback signals that improve models.

  • Q: How do annotation guidelines affect training data quality?

    Clear guidelines standardize labels, reduce ambiguity, and improve inter-annotator agreement, resulting in higher-quality datasets and better model performance.

  • Q: What’s the difference between a rater and an annotator?

    Annotators create or refine labels in datasets; raters evaluate model outputs and prompts for alignment, safety, and quality—both roles contribute to large language model evaluation.

  • Q: Do I need prior ML experience?

    Not necessarily. Entry-level candidates can start with foundational tasks. ML familiarity helps in senior reviewer and QA roles.

  • Q: What content safety considerations apply?

    You may review sensitive material. Rex.zone enforces privacy safeguards, opt-in policies, and well-defined taxonomies to minimize exposure and ensure compliance.

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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Ready to Shape the Future of AI Data Annotation?

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