Senior AI Data Annotation Jobs in San Francisco

Senior AI data annotation jobs in San Francisco focus on expert-level data labeling and evaluation for AI/ML systems, including RLHF, prompt evaluation, QA review, and training data quality for large language model pipelines. On Rex.zone, you will support real-world model performance improvement by applying annotation guidelines compliance across NLP, computer vision, and content safety labeling workflows for AI labs, tech startups, and annotation vendors. This full-time remote role combines high-precision labeling with reviewer responsibility, helping teams ship safer, more accurate models through scalable evaluation operations.

LinkedIn Job Metadata

About the Role

You will lead and execute senior-level AI data annotation work supporting LLM training pipelines and multi-modal AI/ML systems. Your responsibilities include complex data labeling, RLHF preference ranking, prompt and response evaluation, and QA evaluation to improve training data quality and model performance improvement. You will collaborate with operations, engineering, and research stakeholders to interpret annotation guidelines, resolve edge cases, and ensure consistent rubric application across projects for NLP, computer vision, and content safety labeling.

Key Responsibilities

Own high-complexity labeling tasks (text, image, and multimodal) with strict annotation guidelines compliance; perform RLHF tasks such as preference comparisons, ranking, and rationale-based evaluation; conduct prompt evaluation and LLM response grading for helpfulness, honesty, and safety; execute QA evaluation by auditing labeled datasets, measuring inter-annotator agreement, and correcting systematic errors; apply named entity recognition and entity linking standards where required; support computer vision annotation (bounding boxes, polygons, keypoints) and verification workflows; label and review content safety labeling categories (hate, harassment, self-harm, sexual content, violence) per policy; document edge cases, propose rubric clarifications, and improve taxonomy and ontology; communicate quality risks and provide feedback loops that drive model performance improvement.

Required Qualifications

Mid-Senior experience in AI data annotation, data labeling, moderation, evaluation, or QA operations; strong understanding of training data quality, annotation rubrics, and reviewer workflows; demonstrated experience with LLM evaluation, prompt evaluation, or RLHF-style preference tasks; ability to apply consistent judgment under detailed policies for content safety labeling; comfort working with ambiguous examples and escalating edge cases with clear written rationale; strong English writing and reading comprehension suitable for rubric-based grading; familiarity with NLP concepts such as named entity recognition and classification is preferred; familiarity with computer vision annotation tools and schemas is a plus.

Tools and Workflow

Work is completed in remote annotation platforms and QA dashboards with task queues, rubric checklists, and reviewer sampling. You will follow versioned annotation guidelines, contribute to calibration sessions, and use structured feedback to reduce disagreement and improve dataset consistency. Projects may include NLP classification, NER, prompt-response grading, RLHF ranking, content safety labeling, and computer vision annotation depending on client needs.

What Success Looks Like

High accuracy and consistency against the rubric; measurable improvements in training data quality and reduced rework; strong reviewer notes that clarify edge cases and accelerate annotation guidelines compliance; effective collaboration with QA and project leads; reliable throughput while maintaining precision on complex tasks; clear documentation that helps downstream model training pipelines and evaluation benchmarks.

Compensation and Employment Details

This is a full-time remote position in the US with annual pay in USD. Salary range is 63360 to 126720 per year, depending on skills alignment, project scope, and performance. Remote roles remain explicitly Remote.

How to Apply on Rex.zone

Apply through Rex.zone by submitting your profile, annotation or evaluation experience, and relevant work samples if available. Highlight experience with RLHF, LLM evaluation, QA evaluation, and any domain specialization such as NLP, computer vision annotation, or content safety labeling.

Frequently Asked Questions

Q: What are senior AI data annotation jobs in San Francisco?
They are mid-senior roles focused on training data quality for AI/ML systems. Typical work includes advanced data labeling, reviewer-level QA evaluation, RLHF preference ranking, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling to support LLM training pipelines and model performance improvement.
Q: Is this role remote even though it references San Francisco?
Yes. The Remote Type is Remote and remains explicitly Remote. The San Francisco keyword reflects job search intent and market alignment, while the work is performed remotely within the US.
Q: What is RLHF and how does it relate to data annotation?
RLHF (Reinforcement Learning from Human Feedback) is a method where humans evaluate and rank model outputs, creating preference data used to improve LLM behavior. In annotation workflows, this includes pairwise comparisons, ranking, and rubric-based grading that directly supports model training pipelines.
Q: What skills should I emphasize to match this job?
Emphasize AI data annotation, data labeling, RLHF, LLM evaluation, prompt evaluation, QA evaluation, annotation guidelines compliance, training data quality, named entity recognition, computer vision annotation, content safety labeling, and taxonomy/ontology work.
Q: What types of employers use this kind of work?
AI labs, tech startups, enterprise teams, BPOs, and annotation vendors use senior annotation and evaluation operations to scale dataset creation, benchmarking, and safety review for NLP, computer vision, and LLM systems.
Q: How do you measure quality in annotation and evaluation work?
Quality is typically measured through rubric adherence, QA sampling, calibration outcomes, inter-annotator agreement, error taxonomies, rework rates, and whether labeled data leads to measurable model performance improvement on evaluation benchmarks.

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