Senior Data Annotation Jobs in Atlanta

Senior Data Annotation Jobs in Atlanta on Rex.zone focus on improving AI/ML training pipelines through data labeling, RLHF evaluation, and training data quality review. In this full-time remote role, you will apply annotation guidelines compliance, QA evaluation, and prompt evaluation to produce high-quality datasets for large language model evaluation, NLP tasks like named entity recognition, and computer vision annotation. You will collaborate with engineers and research teams to drive model performance improvement, reduce label noise, and support content safety labeling workflows. If you are seeking remote, full-time, mid-senior data annotation work connected to real production ML systems, explore and apply via Rex.zone.

Job Heading: Senior Data Annotation Jobs in Atlanta

About the Role

You will lead and execute senior-level data annotation and evaluation work that directly impacts model training and deployment readiness. This includes creating and refining annotation guidelines, resolving ambiguous edge cases, and performing QA evaluation to ensure training data quality across NLP, computer vision annotation, and content safety labeling. You will support large language model evaluation efforts, including RLHF preference labeling and prompt evaluation, and help connect labeling outputs to model performance improvement metrics.

Key Responsibilities

Own complex data labeling tasks across modalities (text, image, and mixed inputs); perform RLHF ranking, preference labeling, and rubric-based evaluation; execute QA evaluation audits and enforce annotation guidelines compliance; review and adjudicate disagreements, escalating policy gaps and proposing guideline updates; support named entity recognition and taxonomy/ontology alignment for NLP datasets; contribute to content safety labeling, policy mapping, and severity calibration; partner with engineering to improve labeling tools, workflow efficiency, and sampling strategies; document decisions to ensure traceability in LLM training pipelines.

Required Qualifications

Demonstrated experience in data annotation or data labeling at mid-senior level with measurable quality outcomes; strong understanding of training data quality concepts (precision/recall tradeoffs, consistency, inter-annotator agreement); hands-on familiarity with large language model evaluation, RLHF workflows, and prompt evaluation patterns; experience with NLP tasks such as named entity recognition and text classification; ability to interpret ambiguous content and apply policy consistently for content safety labeling; strong written communication for guideline updates, edge-case documentation, and QA reporting.

Preferred Qualifications

Experience with computer vision annotation (bounding boxes, segmentation, keypoints) and multi-pass QA methods; background working with annotation vendors, BPOs, or AI labs with production-scale throughput targets; familiarity with evaluation design (rubrics, gold sets, audit sampling) and error taxonomy; exposure to data operations metrics (defect rate, rework rate, acceptance rate) tied to model performance improvement; comfort collaborating with engineers on tooling improvements for LLM training pipelines.

Tools and Workflows

You may work in web-based labeling tools and internal platforms, using structured rubrics and gold datasets for QA evaluation. Work includes preference ranking for RLHF, prompt evaluation for instruction-following behavior, and systematic adjudication for annotation guidelines compliance. Datasets may span named entity recognition, content safety labeling, and computer vision annotation depending on project needs.

Why This Role on Rex.zone

Rex.zone connects experienced annotators to real AI/ML training workflows used by tech startups, annotation vendors, and AI labs. This senior track emphasizes training data quality, consistent QA evaluation, and practical collaboration that improves large language model evaluation outcomes while maintaining remote, full-time stability.

How to Apply

Apply through Rex.zone with a resume highlighting senior data annotation experience, QA evaluation examples, and any RLHF, prompt evaluation, named entity recognition, computer vision annotation, or content safety labeling work. Include brief notes on the annotation guidelines compliance methods you have used and how you improved training data quality or model performance improvement.

Frequently Asked Questions

Q: Are these senior data annotation jobs in Atlanta remote?
Yes. This posting is explicitly Remote and designed for full-time remote delivery while targeting candidates searching for senior data annotation jobs in Atlanta.
Q: What does “senior” mean in data annotation work?
Senior data annotation typically includes ownership of complex edge cases, QA evaluation leadership, guideline refinement, adjudication, and consistent annotation guidelines compliance that improves training data quality.
Q: Will I work on RLHF and large language model evaluation?
Yes. The role includes RLHF-related preference labeling and large language model evaluation tasks, including prompt evaluation and rubric-based scoring where applicable.
Q: Which domains are covered (NLP, computer vision, content safety)?
Projects may include NLP tasks like named entity recognition, computer vision annotation, and content safety labeling, depending on customer model needs and LLM training pipelines.
Q: Is this full-time employment or contract/freelance?
This job is FULL_TIME. Rex.zone may also list contract or freelance roles, but this posting is structured for full-time remote work.
Q: What skills are most important to be successful?
Core skills include data annotation, data labeling, training data quality review, QA evaluation, prompt evaluation, RLHF workflows, named entity recognition, computer vision annotation, content safety labeling, and understanding of LLM training pipelines.
Q: How is quality measured in data annotation?
Quality is typically measured through QA evaluation using audits, gold sets, inter-annotator agreement, defect/rework rates, and adherence to annotation guidelines compliance aligned to model performance improvement goals.
Q: How do I apply through Rex.zone?
Submit your application through Rex.zone with relevant experience, examples of guideline work, and any history of RLHF, prompt evaluation, named entity recognition, computer vision annotation, or content safety labeling.

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