Entry-Level Data Labeling Jobs

Entry-Level Data Labeling Jobs on Rex.zone connect you with AI/ML training workflows that power RLHF, large language model evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling. As a data labeling contributor, you ensure training data quality and annotation guidelines compliance to drive model performance improvement across modern LLM training pipelines. Explore remote, contract, freelance, part-time, and full-time roles with AI labs, tech startups, BPOs, and annotation vendors, all anchored on the Rex.zone platform.

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Key Responsibilities

Create accurate labels for text, images, audio, and video following detailed annotation guidelines. Execute named entity recognition (NER), intent classification, sentiment tagging, bounding boxes, segmentation, and content safety labeling. Perform prompt evaluation and LLM response grading for RLHF tasks. Conduct basic QA checks to validate consistency and completeness, escalate edge cases, and document ambiguities. Maintain throughput targets and high inter-annotator agreement while contributing to training data quality and model performance improvement.

Required Qualifications

Attention to detail, strong reading comprehension, and reliable execution of instructions. Comfortable working with web-based annotation tools and spreadsheets. Basic understanding of AI/ML concepts and how labeled data trains models. English proficiency and clear written communication. Ability to follow annotation guidelines compliance and meet daily productivity goals. Eligibility to work in remote or onsite setups depending on employer policy.

Preferred Skills

Prior experience with data annotation or content moderation. Familiarity with NLP tasks (NER, intent, sentiment), computer vision labeling (bounding boxes, polygons), and content safety review. Exposure to LLM evaluation, prompt quality assessment, and RLHF pipelines. Basic scripting or automation skills (e.g., Python) are a plus, as is experience with QA evaluation and inter-annotator agreement metrics.

Domains You May Work In

Natural Language Processing (NLP), computer vision, speech/audio, and multi-modal datasets. Common projects include named entity recognition, text classification, dialogue evaluation, content safety labeling, OCR cleanup, and image segmentation. You may also support LLM training pipelines via prompt evaluation and large language model evaluation tasks.

Tools & Platforms

Use industry-standard annotation tools and Rex.zone-integrated workflows for labeling, QA evaluation, and task management. Typical stacks include browser-based labeling UIs, CSV/JSON templates, simple QA dashboards, communication tools (Slack/Jira), and secure data access protocols. Training and guidelines are provided per project.

Work Modalities & Employers

Roles available as remote, hybrid, or onsite; contract, freelance, part-time, or full-time. Employers include AI labs, tech startups, BPOs, and specialized annotation vendors. Entry-level positions often offer flexible schedules and global hiring with clear ramp-up paths.

Quality & Impact

Your labeling accuracy directly influences training data quality, annotation guidelines compliance, and model performance improvement. High-quality annotations increase model reliability, reduce bias, and enhance user safety—especially in content safety labeling and RLHF evaluation.

Career Growth

Grow from entry-level annotator to QA specialist, lead reviewer, domain expert (NLP or computer vision), prompt evaluation analyst, or LLM evaluation specialist. With performance and experience, you can progress into project coordination or data operations roles across AI/ML teams.

Compensation & Benefits

Comp varies by region, modality (contract vs. full-time), and domain complexity. Projects may pay per task, hourly, or salary. Top performers gain access to higher-complexity assignments with better rates and opportunities for consistent work.

How to Apply on Rex.zone

Create your Rex.zone profile, verify skills, and complete sample tasks to demonstrate accuracy and throughput. Browse active jobs, filter by remote, contract, freelance, part-time, or full-time, and submit applications directly. Our platform matches you with AI labs, startups, BPOs, and annotation vendors.

Frequently Asked Questions

  • Q: What is an entry-level data labeling job?

    An entry-level data labeling job involves tagging and categorizing data (text, images, audio, video) to train AI models. Tasks include NER, bounding boxes, sentiment, content safety labeling, prompt evaluation, and LLM response grading.

  • Q: Do I need prior annotation experience?

    Not required. You should be detail-oriented, follow instructions, and learn project-specific guidelines. Rex.zone provides onboarding materials and practice tasks.

  • Q: Is remote work available?

    Yes. Many roles are remote, with flexible schedules in contract, freelance, part-time, and full-time formats, depending on the employer.

  • Q: What domains can I work in?

    NLP (NER, classification), computer vision (bounding boxes, polygons), speech/audio, content safety, and LLM training pipelines via RLHF and evaluation.

  • Q: How is quality measured?

    Quality is measured via accuracy, consistency, inter-annotator agreement, guideline adherence, and QA review results. Performance can unlock higher-paying projects.

  • Q: What tools will I use?

    Browser-based labeling UIs, spreadsheets, QA dashboards, and Rex.zone-integrated workflows. Training and guidelines are provided per project.

  • Q: How do I apply?

    Create a Rex.zone profile, complete sample tasks, and apply to listed roles. You can filter by remote, contract, freelance, part-time, or full-time.

  • Q: Are there growth opportunities?

    Yes. You can progress to QA specialist, team lead, domain expert, prompt evaluator, or LLM evaluation roles with performance and experience.

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