Senior AI Data Annotation Jobs in Austin

Senior AI data annotation jobs in Austin focus on building high-quality training data for AI/ML systems through data labeling, RLHF, prompt evaluation, and QA evaluation. On Rex.zone, you will support large language model evaluation and LLM training pipelines by applying annotation guidelines compliance, auditing training data quality, and improving model performance. This remote, full-time role works across NLP and computer vision annotation, including named entity recognition, content safety labeling, and multimodal tasks. Explore and apply on Rex.zone to help AI labs, tech startups, and annotation vendors ship safer, more reliable models at scale.

Job Heading: Senior AI Data Annotation Jobs in Austin

About the Role

You will lead and execute senior-level AI data annotation work for production AI/ML training workflows. Projects include RLHF data collection, prompt evaluation, LLM response ranking, QA evaluation for policy compliance, and computer vision annotation. You will translate task specs into clear annotation guidelines, calibrate edge cases with reviewers, and drive training data quality to support model performance improvement.

What You’ll Do

Perform expert AI data annotation and data labeling for NLP, LLM training, and computer vision datasets. Run RLHF workflows: preference ranking, pairwise comparisons, critique writing, and rubric-based scoring. Execute prompt evaluation and response evaluation to identify failure modes, hallucinations, and unsafe outputs. Apply content safety labeling for toxicity, self-harm, hate, harassment, sexual content, and policy violations. Conduct QA evaluation and audits to ensure annotation guidelines compliance and consistent labeling decisions. Create and maintain decision logs for ambiguous cases; propose clarifications to improve reviewer alignment. Monitor training data quality metrics and sampling strategies that support model performance improvement. Collaborate with engineering and AI/ML stakeholders to integrate labeled data into LLM training pipelines.

Core Workstreams (Entity Coverage)

RLHF (Reinforcement Learning from Human Feedback): preference data, reward modeling signals, rubric design inputs. LLM evaluation: prompt evaluation, factuality checks, policy compliance checks, and instruction-following grading. NLP labeling: named entity recognition, intent classification, semantic similarity, and conversation tagging. Computer vision annotation: bounding boxes, polygons, keypoints, segmentation masks, and multi-label attributes. Content safety labeling: platform policy taxonomies, risk severity scoring, and safety QA review.

Requirements

Mid-Senior experience level with hands-on AI data annotation, data labeling, or evaluation workflows. Strong judgment and consistency when applying annotation guidelines to edge cases and nuanced content. Demonstrated experience with QA evaluation, error analysis, and training data quality improvement. Comfort working with LLM outputs, prompt evaluation tasks, and RLHF-style ranking/scoring frameworks. Ability to communicate clearly in written form and maintain accurate documentation for auditability.

Nice to Have

Experience with named entity recognition, taxonomy design, and policy-driven content safety labeling. Experience with computer vision annotation tools and complex geometries (polygons/segmentation). Exposure to model evaluation concepts (precision/recall, inter-annotator agreement, calibration exercises). Background collaborating with AI labs, tech startups, BPOs, annotation vendors, or data operations teams.

Why This Role on Rex.zone

Remote, full-time work supporting real AI/ML training pipelines and LLM training programs. Opportunities to contribute across NLP, computer vision, and content safety labeling domains. Clear operating cadence: guidelines, calibration, QA evaluation, and continuous training data quality improvements. Work that directly impacts model reliability, safety, and overall model performance improvement.

How to Apply

Visit Rex.zone and open the listing for Senior AI Data Annotation Jobs in Austin. Highlight experience in data labeling, RLHF, prompt evaluation, QA evaluation, and guideline compliance. Share examples of training data quality work: audits, calibration notes, error analyses, or rubric usage.

Frequently Asked Questions

Q: Are these senior AI data annotation jobs in Austin remote?
Yes. The role is explicitly marked Remote and is open to candidates in the US while aligned to Austin-based hiring intent.
Q: What type of annotation work is included?
Work includes AI data annotation and data labeling across RLHF, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling for LLM training pipelines.
Q: Is this full-time or contract/freelance?
This posting is FULL_TIME. Rex.zone may also list contract or freelance annotation roles separately, but this role is full-time.
Q: What does “senior” mean for data annotation?
Senior scope typically includes advanced edge-case handling, guideline refinement, QA evaluation/auditing, calibration leadership, and measurable training data quality improvements that support model performance improvement.
Q: Which domains are most common: NLP or computer vision?
Both are supported. Many projects focus on NLP and large language model evaluation, with additional computer vision annotation and multimodal labeling depending on client needs.
Q: What employers commonly hire for this role type?
AI labs, tech startups, BPOs, annotation vendors, and technology teams running LLM training and evaluation programs commonly hire for senior AI data annotation roles.

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