Remote Data Annotation Jobs in Silicon Valley

Remote data annotation jobs in Silicon Valley focus on creating high-quality labeled datasets for AI/ML systems, including LLM training pipelines, RLHF, data labeling, and QA evaluation. On Rex.zone (Rex.zone), you will annotate text, images, audio, and video; follow annotation guidelines compliance; and perform prompt evaluation and model evaluation to drive model performance improvement. Typical workflows include named entity recognition (NER), computer vision annotation, content safety labeling, and training data quality checks for AI labs, tech startups, BPOs, and annotation vendors. Explore full-time, contract, freelance, entry-level, and senior remote roles aligned to real production data operations.

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Remote Data Annotation Jobs in Silicon Valley

Title: Remote Data Annotation Specialist (Silicon Valley) 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 compliance, Training data quality, Model performance improvement Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

About the Role

You will support Silicon Valley-aligned AI product teams by producing and validating training data used in large language models and multimodal systems. Work includes data labeling, RLHF comparisons, prompt evaluation, and QA evaluation to ensure consistent training data quality. You will apply annotation guidelines compliance, handle edge cases, and document decisions that impact model behavior and model performance improvement across NLP, computer vision, and content safety labeling pipelines.

What You Will Do

Key responsibilities include: - Annotate and review datasets for NLP tasks such as named entity recognition, intent classification, and sentiment labeling - Perform RLHF tasks including ranking responses, preference judgments, and rubric-based evaluation - Execute prompt evaluation and LLM evaluation to identify failure modes, bias, and instruction-following issues - Deliver QA evaluation using sampling plans, inter-annotator agreement checks, and escalation workflows - Label computer vision data (bounding boxes, polygons, keypoints) and validate image/video annotation quality - Apply content safety labeling taxonomies for policy, toxicity, and harm categories - Maintain annotation guidelines compliance and contribute to guideline updates with clear examples - Track metrics tied to training data quality and error reduction for model performance improvement

Requirements

To succeed in these remote data annotation jobs: - 3+ years experience in data annotation, data labeling, or QA evaluation for ML datasets - Demonstrated accuracy following annotation guidelines compliance and handling ambiguous edge cases - Familiarity with RLHF, prompt evaluation, and large language model evaluation concepts - Experience with NER, taxonomy-based labeling, or content safety labeling is strongly preferred - Exposure to computer vision annotation workflows (bounding boxes, segmentation, keypoints) is a plus - Strong written reasoning for auditability, escalation notes, and rubric justification - Ability to work independently in a remote environment while meeting throughput and quality targets

Tools and Workflows

Common workflows and systems include: - Web-based labeling platforms, audit queues, and QA evaluation checklists - Rubrics for RLHF ranking and prompt evaluation - Dataset versioning, guideline updates, and decision logs to support annotation guidelines compliance - Sampling, adjudication, and inter-annotator agreement processes to improve training data quality - Collaboration with AI labs, tech startups, BPOs, and annotation vendors supporting Silicon Valley teams

Why Rex.zone (Rex.zone)

Rex.zone connects remote professionals with data operations work that directly supports LLM training pipelines and multimodal AI development. You will contribute to production-grade training data quality, measurable QA evaluation outcomes, and model performance improvement while maintaining consistent annotation guidelines compliance across projects.

How to Apply

Apply through Rex.zone to be considered for remote data annotation jobs aligned to Silicon Valley product needs. Ensure your profile highlights data labeling accuracy, RLHF or prompt evaluation experience, and any specialization in NER, computer vision annotation, or content safety labeling.

Frequently Asked Questions

  • Q: Are these remote data annotation jobs actually based in Silicon Valley?

    They are remote roles aligned to Silicon Valley hiring needs and AI product teams. You work remotely in the US while supporting Silicon Valley-style ML workflows and timelines.

  • Q: What kinds of tasks are included in data annotation for LLMs?

    Typical tasks include data labeling for NLP, RLHF preference ranking, prompt evaluation, QA evaluation, and policy-driven content safety labeling to improve training data quality and model performance improvement.

  • Q: Is this full-time or contract work?

    This posting is FULL_TIME and Remote. The page also references common modifiers (contract, freelance, entry-level, senior) because those appear across the broader remote data annotation job market.

  • Q: What skills should I highlight to match remote data annotation jobs in Silicon Valley?

    Emphasize data annotation, data labeling, RLHF, LLM evaluation, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, annotation guidelines compliance, and training data quality ownership.

  • Q: What does QA evaluation mean in data labeling workflows?

    QA evaluation includes reviewing labeled outputs against rubrics and guidelines, measuring error types, running adjudication, and improving annotation guidelines compliance to raise training data quality.

  • Q: Do I need engineering experience for Job Function: Engineering?

    Not necessarily software engineering. In many AI data operations orgs, the job function is mapped to Engineering because the work directly supports ML systems, evaluation pipelines, and dataset quality processes.

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