Entry-Level Data Labeling Jobs is a hiring entity on Rex.zone for candidates who want to start in AI/ML production workflows. These roles support LLM training pipelines and RLHF by performing data labeling, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling. The goal is training data quality and annotation guidelines compliance that drive model performance improvement and large language model evaluation. Apply for remote, contract, freelance, or full‑time openings with AI labs, tech startups, BPOs, and annotation vendors. Explore opportunities across NLP, computer vision, and safety teams, and begin a structured path from entry-level tasks to specialized labeling and QA leadership.
You will annotate text, images, and audio, tag entities, classify intents, draw bounding boxes, rate prompts and completions, and review content for safety and policy compliance. Work inside standardized workflows that feed model training, evaluation, and RLHF loops.
Follow annotation guidelines, ensure training data quality, complete checklist-based QC, escalate edge cases, and document decisions. Contribute to annotation guidelines compliance, model performance improvement, and large language model evaluation through reliable gold labels.
Strong attention to detail, fluent written English, basic ML literacy, ability to follow instructions, time management, and consistency. Bonus: familiarity with NLP tasks (NER, sentiment), computer vision basics, content safety policies, and spreadsheet hygiene.
Typical tools include Label Studio, CVAT, Prodigy, and internal dashboards. Work within dataset versioning, taxonomy updates, inter-annotator agreement checks, QA sampling, audit trails, and prompt evaluation queues used in LLM training pipelines and RLHF.
NLP (named entity recognition, summarization, sentiment), computer vision (object detection, segmentation), speech/audio tagging, content safety labeling, and prompt evaluation for LLM training. Roles exist across research, production, and QA evaluation teams.
Openings include remote, hybrid, and on-site. Contract, freelance, and full-time tracks are available. While focused on entry-level, growth paths lead to specialist, QA lead, and senior reviewer roles.
Discover roles with AI labs, tech startups, BPOs, and annotation vendors. Projects range from data bootstrapping to model evaluation and content safety operations supporting global AI products.
Competitive entry-level rates; pay varies by project complexity, speed, and quality. Some projects offer bonuses for accuracy, shift differentials, and advancement to reviewer and quality-control tiers.
Create your Rex.zone profile, verify your email, complete the guidelines quiz, pass a short sample task, and choose projects by domain (NLP, vision, content safety). Successful candidates receive onboarding and task access.
Advance from annotator to reviewer, QA lead, or prompt evaluator. Specialize in named entity recognition, content safety policy, or computer vision annotation, and contribute to model evaluation and RLHF feedback cycles.
You create accurate labels for text, images, audio, and video using clear guidelines. Tasks include NER tagging, intent classification, sentiment analysis, bounding boxes, content safety reviews, and prompt evaluation to improve training data quality.
Most projects on Rex.zone offer remote options, with some hybrid or on-site roles depending on employer requirements. You can filter openings by remote, contract, freelance, or full-time.
No. Entry-level candidates are welcome. You’ll complete a short onboarding, guidelines quiz, and sample task. Accuracy, consistency, and attention to detail are more important than prior industry experience.
Common tools include Label Studio, CVAT, Prodigy, and internal platforms. You’ll also work with spreadsheets for metadata, QC dashboards, and issue trackers used in LLM training pipelines.
Evaluation includes inter-annotator agreement, reviewer checks, sampling-based QA, and audit trails. Scores reflect annotation guidelines compliance, accuracy, speed, and contribution to model performance improvement.
RLHF uses human feedback to improve model behavior. Labelers rate outputs, compare responses, and flag policy violations, providing structured signals that drive large language model evaluation and tuning.
Terms vary by employer (AI labs, tech startups, BPOs, annotation vendors). Entry-level roles may pay hourly or per-task, with quality bonuses and opportunities to move into reviewer or QA roles.
Create a profile, verify your email, pass the guidelines quiz and a sample task, then select projects by domain (NLP, computer vision, content safety). You’ll be matched to openings that fit your skills and availability.
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