Remote Data Annotation Jobs in San Diego

Remote data annotation jobs in San Diego focus on training-data creation for modern AI systems. At Rex.zone, you will label and evaluate text, images, and audio to support LLM training pipelines, RLHF (Reinforcement Learning from Human Feedback), data labeling QA evaluation, prompt evaluation, and structured tasks like named entity recognition. Your work improves training data quality, annotation guidelines compliance, and model performance improvement for AI labs, tech startups, and annotation vendors. This is a full-time remote role aligned to real production workflows, including computer vision annotation, content safety labeling, and large language model evaluation, with clear quality targets and feedback loops.

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Remote Data Annotation Jobs in San Diego — Role Overview

Title: Remote Data Annotation Specialist (San Diego) Date: 25-02-2026 Company: Rex.zone Country: US Remote Type: Remote Employment Type: FULL_TIME Experience Level: Mid-Senior Industry: Technology Job Function: Engineering Skills: Data Annotation, Data Labeling, RLHF, LLM Evaluation, Prompt Evaluation, QA Evaluation, Named Entity Recognition, Computer Vision Annotation, Content Safety Labeling, Annotation Guidelines Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR You will deliver high-precision annotations and evaluations used to train and validate machine learning models. Work spans NLP labeling (classification, entity tagging, summarization), RLHF preference ranking, prompt-response evaluation, and quality assurance checks to ensure training data quality. You will follow annotation guidelines compliance requirements, document edge cases, and collaborate asynchronously with remote reviewers and project leads.

What You Will Do

Execute data labeling tasks across NLP and computer vision annotation projects, including bounding boxes, segmentation, attribute tagging, and text span labeling. Perform RLHF and large language model evaluation by ranking responses, assessing helpfulness/harmlessness, and identifying instruction-following failures. Run QA evaluation workflows: self-checks, peer reviews, gold-standard audits, and consistency checks to improve annotation reliability. Apply content safety labeling taxonomies (policy-based categories) and escalate uncertain cases using defined escalation paths. Write clear rationales for labels, capture ambiguity, and propose guideline updates when recurring edge cases appear. Track throughput and quality metrics (accuracy, agreement rates, defect density) and iterate to improve model performance improvement.

Required Qualifications

Mid-Senior experience in data annotation, data labeling, QA evaluation, or LLM evaluation in production or vendor environments. Strong reading comprehension and attention to detail for prompt evaluation, instruction-following assessment, and policy-based content safety labeling. Comfort working with annotation tools, task queues, and versioned guidelines; ability to maintain annotation guidelines compliance. Ability to explain decisions with concise written rationales and resolve ambiguous labeling scenarios. Familiarity with NLP concepts such as named entity recognition, intent classification, and taxonomy-driven labeling is strongly preferred.

Preferred Qualifications

Experience with RLHF workflows (preference ranking, pairwise comparisons, rubric-based scoring) and evaluation sets. Exposure to computer vision annotation tasks (bounding boxes, polygons, keypoints) and inter-annotator agreement measurement. Understanding of LLM training pipelines, dataset curation, sampling strategies, and error analysis. Background in content moderation, trust & safety, or policy operations related to content safety labeling. Ability to mentor annotators, contribute to calibration sessions, and support quality programs at scale.

Work Arrangement and Location

This is a Remote, FULL_TIME position aligned to remote data annotation jobs in San Diego (US). You may work from San Diego or anywhere in the US where Rex.zone operates, following project-specific scheduling and data access requirements. Remote roles remain Remote, and collaboration is primarily asynchronous with periodic calibration and QA review sessions.

How to Apply on Rex.zone

Navigate to Rex.zone to review active remote data annotation jobs in San Diego, select the project track (NLP, computer vision annotation, content safety labeling, or RLHF evaluation), and submit your application. Ensure your experience highlights training data quality work, annotation guidelines compliance, and any large language model evaluation or QA evaluation exposure.

Frequently Asked Questions

  • Q: What are remote data annotation jobs in San Diego?

    They are remote roles focused on creating and evaluating labeled datasets used to train AI/ML models. Typical tasks include data labeling for NLP and computer vision annotation, QA evaluation for training data quality, and RLHF or prompt evaluation for large language model evaluation.

  • Q: Is this role fully remote?

    Yes. The Remote Type is Remote and the position is designed for distributed work while supporting US-based projects aligned with remote data annotation jobs in San Diego.

  • Q: What types of tasks will I work on?

    You may work on named entity recognition, text classification, prompt-response scoring, RLHF preference ranking, content safety labeling, and computer vision annotation tasks such as bounding boxes and segmentation, plus QA evaluation and audit workflows.

  • Q: What skills should match the job intent for remote data annotation work?

    Strong data annotation and data labeling fundamentals, annotation guidelines compliance, careful rubric-based judgment for LLM evaluation and prompt evaluation, and disciplined QA evaluation practices that improve training data quality and model performance improvement.

  • Q: Is this contract or freelance?

    This posting is FULL_TIME. Rex.zone may also list contract or freelance roles separately; check Rex.zone for other remote, contract, freelance, entry-level, or senior openings.

  • Q: What industries and employers use data annotation and RLHF workflows?

    AI labs, technology companies, tech startups, BPOs, and annotation vendors rely on data labeling, RLHF, and evaluation pipelines to improve model performance and deploy reliable AI products.

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