Remote Data Annotator Jobs in San Francisco

Rex.zone is recruiting for remote data annotator jobs supporting AI/ML training workflows used by AI labs, tech startups, and annotation vendors. As a Data Annotator, you will label text, images, and conversations to produce high-quality training data, follow annotation guidelines compliance, and complete QA evaluation to improve model performance. Daily work may include RLHF preference ranking, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling for large language model evaluation. These full-time remote roles are designed for consistent throughput, training data quality, and measurable accuracy in LLM training pipelines—helping teams ship safer, more reliable models while you work from anywhere in the US.

Remote Data Annotator Jobs in San Francisco

About the Role

You will produce and validate labeled datasets used in NLP and computer vision systems, including large language model evaluation and safety tuning. Work includes data labeling for classification, extraction, and ranking tasks; applying detailed labeling taxonomies; and performing QA evaluation to meet training data quality targets. You will annotate prompts and model outputs, perform RLHF preference comparisons, identify policy violations for content safety labeling, and document edge cases to improve annotation guidelines compliance. You will collaborate asynchronously with project leads on Rex.zone to interpret instructions, triage ambiguous examples, and ensure consistent outputs across batches.

Key Responsibilities

Deliver accurate data annotation for text, image, and multimodal tasks in LLM training pipelines; Execute RLHF workflows such as preference ranking and reward model data creation; Perform prompt evaluation and response grading using rubric-based criteria; Apply named entity recognition and structured extraction labels with high consistency; Complete computer vision annotation such as bounding boxes, polygons, keypoints, and attribute tagging when assigned; Conduct QA evaluation including spot checks, disagreement resolution, and error categorization; Maintain annotation guidelines compliance and document edge cases, guideline gaps, and taxonomy updates; Protect sensitive data and follow content safety labeling policies for harmful, unsafe, or restricted content; Meet throughput and accuracy goals while maintaining stable inter-annotator agreement.

Required Qualifications

3+ years in data labeling, data annotation, QA evaluation, or adjacent roles in AI/ML operations; Strong understanding of rubric-based evaluation, annotation guidelines compliance, and training data quality concepts; Experience with at least one of: RLHF, prompt evaluation, content safety labeling, named entity recognition, or computer vision annotation; High attention to detail with repeatable decision-making and clear written rationale; Ability to work independently in a full-time remote environment with reliable schedule coverage; Comfortable reviewing model outputs, identifying failure modes, and escalating unclear cases.

Preferred Qualifications

Experience evaluating large language model behavior for helpfulness, harmlessness, and honesty; Familiarity with dataset versioning, gold sets, adjudication workflows, and inter-annotator agreement tracking; Background in NLP, computational linguistics, or applied QA for ML products; Experience working with AI labs, tech startups, BPOs, or annotation vendors on production labeling pipelines; Exposure to multilingual annotation, domain-specific taxonomies, or safety policy operations.

What Success Looks Like

High accuracy and consistency across tasks with strong annotation guidelines compliance; Clear, actionable notes on ambiguous examples that improve labeling rubrics; Reliable throughput without sacrificing training data quality; Reduced rework via proactive QA evaluation and error trend reporting; Tangible model performance improvement signals from cleaner RLHF and evaluation data.

Work Arrangement

Remote Type: Remote (US) with full-time availability. The role is listed for San Francisco search intent and recruiting needs, while work is performed remotely. You may collaborate with distributed teams across time zones using Rex.zone workflow tools.

How to Apply on Rex.zone

Visit Rex.zone and search this page title to explore current remote data annotator jobs aligned with San Francisco recruiting needs. Submit your profile, highlight data labeling and QA evaluation experience, and include examples of guideline-based decision-making for RLHF, prompt evaluation, or content safety labeling.

Frequently Asked Questions

Q: Are these remote data annotator jobs located in San Francisco?
These roles are remote and available in the US. The page targets San Francisco recruiting and search intent, while the work itself remains explicitly Remote.
Q: What types of tasks will I annotate?
Typical tasks include data labeling for NLP and computer vision, named entity recognition, prompt evaluation, RLHF preference ranking, QA evaluation, and content safety labeling for LLM training pipelines.
Q: Is this full-time or contract work?
Employment Type is FULL_TIME for this posting, and the role is Remote.
Q: What skills should I emphasize to get selected?
Emphasize training data quality, annotation guidelines compliance, QA evaluation, RLHF workflows, prompt evaluation, content safety labeling, named entity recognition, and computer vision annotation—plus examples of consistent rubric-based decisions.
Q: How does QA evaluation work in annotation pipelines?
QA evaluation commonly includes gold-set checks, spot audits, disagreement review, error categorization, and guideline updates to improve inter-annotator consistency and reduce rework.
Q: What is RLHF and why is it used?
RLHF (Reinforcement Learning from Human Feedback) uses human preference judgments—like ranking responses—to train reward models that improve large language model behavior and alignment.
Q: What is the salary range for this role?
Salary Currency is USD with Salary Min 63360 and Salary Max 126720 per YEAR, as listed in the job metadata.

230+Domains Covered

120K+PhD, Specialist, Experts Onboarded

50+Countries Represented

Industry-Leading Compensation

We believe exceptional intelligence deserves exceptional pay. Our platform consistently offers rates above the industry average, rewarding experts for their true value and real impact on frontier AI. Here, your expertise isn't just appreciated - it's properly compensated.

Work Remotely, Work Freely

No office. No commute. No constraints. Our fully remote workflow gives experts complete flexibility to work at their own pace, from any country, any time zone. You focus on meaningful tasks - we handle the rest.

Respect at the Core of Everything

AI trainers are the heart of our company. We treat every expert with trust, humanity, and genuine appreciation. From personalized support to transparent communication, we build long-term relationships rooted in respect and care.

Ready to Shape the Future of AI Data Operations?

Apply Now.