Senior Data Annotator Jobs in Seattle

Senior Data Annotator jobs in Seattle focus on producing high-quality training data for AI systems across NLP, computer vision, and large language model evaluation. On Rex.zone, you will label and review data, run RLHF and prompt evaluation workflows, and perform QA checks that improve model performance in real-world AI/ML training pipelines. This is a Remote, full-time role supporting annotation guidelines compliance, content safety labeling, and evaluation tasks that directly impact training data quality and downstream model reliability for AI labs, tech startups, and annotation vendors.

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LinkedIn Job Metadata — Senior Data Annotator Jobs in Seattle

Title: Senior Data Annotator Jobs in Seattle | 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: Senior data annotation, data labeling, RLHF, prompt evaluation, QA evaluation, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, LLM training pipelines, training data quality, model performance improvement | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR

About the Role

You will deliver senior-level annotation and review across text, image, and multimodal datasets, ensuring training data quality for LLM training pipelines. Work includes RLHF preference ranking, prompt evaluation, QA evaluation, and strict adherence to annotation guidelines compliance. You will collaborate asynchronously with distributed teams supporting AI labs, tech startups, BPOs, and annotation vendors, while remaining Remote and full-time.

What You Will Do

Execute complex data labeling and review tasks for NLP, computer vision, and content safety labeling; perform RLHF comparisons and preference judgments to improve large language model evaluation; conduct QA evaluation, audits, and error analysis to raise training data quality; apply named entity recognition and taxonomy-based labeling for structured datasets; document decisions, edge cases, and guideline clarifications to improve annotation guidelines compliance; support model performance improvement by identifying systematic label noise and recommending remediation.

Required Qualifications

Mid-Senior experience in data annotation, data labeling, or QA evaluation; proven ability to follow and refine annotation guidelines compliance with high accuracy; familiarity with RLHF, prompt evaluation, and large language model evaluation concepts; experience with named entity recognition or similar structured labeling tasks; comfort with computer vision annotation or multimodal labeling; strong written judgment for content safety labeling and policy-driven decisions; ability to work Remote in a full-time schedule with consistent throughput.

Preferred Qualifications

Experience supporting LLM training pipelines end-to-end, including sampling, adjudication, and QA gating; demonstrated work on training data quality initiatives and measurement; background in evaluation rubric design, calibration sessions, or reviewer training; domain familiarity in safety, trust, and quality operations; experience working with AI labs, tech startups, BPOs, or annotation vendors.

How Success Is Measured

High agreement and low rework rates; consistent annotation guidelines compliance; measurable improvements in training data quality and QA evaluation pass rates; clear documentation of edge cases and rationale; contributions to model performance improvement through error analysis and feedback loops.

Work Arrangement

Remote, FULL_TIME. Although the keyword targets Seattle, this role remains explicitly Remote and may support teams and projects across the US. You may see related opportunities in remote, contract, freelance, entry-level, and senior tracks on Rex.zone.

Apply on Rex.zone

Explore Senior Data Annotator jobs in Seattle on Rex.zone and apply to Remote full-time roles supporting RLHF, data labeling, QA evaluation, and LLM training pipelines. Keep your profile updated with relevant annotation projects, tooling familiarity, and examples of guideline-driven decision making.

Frequently Asked Questions

  • Q: Is this role Remote or based in Seattle?

    This job is explicitly Remote. The page targets “Senior Data Annotator jobs in Seattle” as a search modifier, but the work arrangement remains Remote and full-time.

  • Q: What types of annotation tasks are included?

    Typical work includes data labeling for NLP and computer vision annotation, named entity recognition, content safety labeling, RLHF preference ranking, prompt evaluation, and QA evaluation tied to training data quality.

  • Q: How does this role impact AI/ML systems?

    Your labels and evaluations feed LLM training pipelines and model evaluation loops, directly influencing model performance improvement, safety behavior, and reliability through higher-quality training data.

  • Q: Is this position full-time, contract, or freelance?

    This posting is FULL_TIME. Rex.zone may also list remote contract and freelance annotation roles, but this specific job is full-time.

  • Q: What experience level is expected?

    The Experience Level is Mid-Senior, aligned with senior-level annotation, reviewer judgment, QA evaluation rigor, and strong annotation guidelines compliance.

  • Q: What skills should I highlight to match this job?

    Highlight senior data annotation, data labeling, RLHF, prompt evaluation, QA evaluation, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, and experience supporting LLM training pipelines with a focus on training data quality.

  • Q: Which employer types does this role support?

    Projects may support AI labs, tech startups, BPOs, and annotation vendors, depending on the dataset and evaluation workflow.

  • Q: What does quality assurance look like in annotation work?

    QA evaluation can include calibration, adjudication, targeted audits, error analysis, and rubric enforcement to reduce label noise and improve training data quality for downstream model behavior.

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