Remote Data Labeling Jobs in San Diego

Remote Data Labeling Jobs in San Diego at Rex.zone focus on training-data creation for AI systems by labeling text, images, audio, and video used in large language model evaluation, RLHF workflows, and computer vision annotation. You will follow annotation guidelines, perform QA evaluation, and complete prompt evaluation tasks that improve model performance and training data quality. This role supports LLM training pipelines, named entity recognition, content safety labeling, and dataset curation for AI labs, tech startups, and annotation vendors. Explore full-time, contract, freelance, entry-level, and senior pathways on Rex.zone while staying fully Remote from the US.

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Remote Data Labeling Jobs in San Diego — Role Overview

Title: Remote Data Labeling 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 labeling, data annotation, RLHF, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, LLM training pipelines Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

What You Will Do

You will create and validate labeled datasets that power model training and evaluation. Core work includes RLHF preference ranking, prompt-response evaluation, rubric-based QA evaluation, named entity recognition for NLP datasets, and computer vision annotation such as bounding boxes, polygons, keypoints, segmentation masks, and image classification. You will also perform content safety labeling (policy categories, risk severity, and refusal correctness) and contribute to training data quality through sampling, adjudication, and error analysis.

Projects and Domains You May Support

Assignments may include NLP tasks (NER, intent, sentiment, toxicity), LLM evaluation (instruction following, factuality, groundedness, reasoning quality), and computer vision annotation (autonomous driving scenes, retail product recognition, medical imagery where permitted). You may also support multilingual evaluation, speech/audio labeling, and workflow improvements such as guideline updates and edge-case taxonomy design.

Required Qualifications

You have professional experience in data labeling or data annotation and can apply annotation guidelines with high consistency. You are comfortable with ambiguity, can justify decisions with evidence, and can self-audit your work to meet training data quality targets. You can communicate clearly in written English, collaborate asynchronously with remote teams, and maintain speed without sacrificing quality. Familiarity with RLHF, QA evaluation, and prompt evaluation for large language models is expected at this level.

Preferred Qualifications

Experience with named entity recognition, taxonomy design, and inter-annotator agreement methods. Exposure to computer vision annotation (bounding boxes, segmentation) and content safety labeling. Prior work supporting AI labs, tech startups, BPOs, or annotation vendors. Ability to perform basic error analysis, write clear rationales, and propose guideline refinements that improve model performance improvement outcomes.

Quality Standards and Evaluation

Success is measured by annotation guidelines compliance, training data quality, QA pass rates, consistency across edge cases, and ability to handle escalations. You will participate in calibration tasks, gold-standard checks, and periodic audits. You will document decisions, flag ambiguous instructions, and help refine rubrics so downstream LLM training pipelines receive reliable labels.

Work Setup (Remote From the US)

This is a fully Remote, FULL_TIME role for candidates in the US, including San Diego. Work is performed online with secure tools and clear throughput/quality targets. You will coordinate with distributed reviewers, follow project-specific confidentiality requirements, and manage tasks in an asynchronous workflow typical of modern data operations teams.

How to Apply on Rex.zone

Apply through Rex.zone to be matched with remote data labeling jobs aligned to your skills in data annotation, RLHF, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling. Your application should highlight prior annotation domains, consistency/quality metrics, and examples of rubric-driven decisions.

Frequently Asked Questions

  • Q: Are these remote data labeling jobs actually remote if I live in San Diego?

    Yes. The Remote Type is Remote, and the role is designed to be completed online from the US, including San Diego.

  • Q: What is data labeling in an LLM training pipeline?

    Data labeling is the process of assigning structured tags and judgments to raw data (text, images, audio, video) so models can learn patterns and be evaluated. In LLM training pipelines, this often includes prompt evaluation, rubric scoring, and RLHF preference comparisons.

  • Q: What does RLHF work look like in this role?

    RLHF tasks typically involve comparing multiple model responses, selecting the better answer based on guidelines, and writing brief rationales. This produces preference data used to improve instruction following and response quality.

  • Q: What kinds of annotation domains are covered?

    Common domains include NLP (named entity recognition, intent, sentiment), computer vision annotation (bounding boxes, segmentation), content safety labeling, and general LLM evaluation (helpfulness, factuality, groundedness, policy compliance).

  • Q: Is this full-time or can it be contract or freelance?

    This posting is FULL_TIME. However, Rex.zone often hosts multiple employment types, including contract and freelance, depending on project needs.

  • Q: Is this role entry-level?

    This posting targets Mid-Senior experience. Rex.zone may also list entry-level and senior roles separately, depending on available programs.

  • Q: How is quality measured for data annotation and QA evaluation?

    Quality is measured through guideline compliance, gold-task accuracy, consistency across edge cases, reviewer feedback, and audit results. Strong performance includes clear rationales and low rework rates.

  • Q: What tools will I use?

    You will use web-based annotation platforms and review tools provided by the project team. Tooling varies by domain (NLP, CV, content safety) but emphasizes secure access, clear rubrics, and auditability.

  • Q: What skills should I highlight to match remote data labeling jobs in San Diego?

    Highlight data labeling, data annotation, RLHF, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, and familiarity with LLM training pipelines and training data quality practices.

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