Senior AI Data Annotation Jobs in Atlanta

Senior AI Data Annotation roles at Rex.zone focus on training-data quality for modern AI systems, including data labeling, RLHF (Reinforcement Learning from Human Feedback), prompt evaluation, and QA evaluation for large language model evaluation. You will apply annotation guidelines compliance to produce high-signal datasets that drive model performance improvement across NLP, computer vision annotation, named entity recognition, and content safety labeling. These Remote, FULL_TIME roles support LLM training pipelines used by AI labs, tech startups, and annotation vendors, with a strong emphasis on training data quality, reviewer calibration, and scalable annotation operations.

Job Opening: Senior AI Data Annotation Specialist (Atlanta, Remote)

About the Role

As a Senior AI Data Annotation Specialist, you will create and validate high-quality labeled data used to train and evaluate LLMs and multimodal models. You will execute complex annotation tasks (text, image, and mixed-modality), run QA evaluation workflows, and support RLHF by rating, ranking, and critiquing model outputs. You will work within production annotation guidelines, document edge cases, and help improve training data quality that directly impacts model performance improvement.

What You’ll Work On

You will contribute to AI/ML training workflows including: large language model evaluation, prompt evaluation, RLHF preference ranking, instruction-following scoring, content safety labeling, named entity recognition and entity linking, text classification and sentiment, computer vision annotation (bounding boxes, polygons, keypoints), multimodal alignment checks, and dataset error analysis. You will help maintain annotation guidelines compliance and continuously raise training data quality through audits and reviewer feedback loops.

Responsibilities

You will: produce accurate labels following annotation guidelines; perform QA evaluation and second-pass reviews; calibrate on rubrics for RLHF and prompt evaluation; identify ambiguity and propose guideline updates; track disagreements and resolve edge cases; perform sampling-based audits to improve training data quality; collaborate with data ops and engineering on tooling feedback; maintain clear documentation for datasets, taxonomy changes, and evaluation criteria; support model performance improvement by reporting systematic failure modes observed during large language model evaluation.

Required Qualifications

You have: experience delivering high-volume, high-precision AI data annotation; strong English comprehension and writing for prompt evaluation and critique; familiarity with RLHF concepts (pairwise ranking, preference data); experience with QA evaluation processes (audits, inter-annotator agreement, reviewer calibration); knowledge of NLP annotation tasks such as named entity recognition; comfort with computer vision annotation tools and concepts; ability to follow and improve annotation guidelines compliance; strong attention to detail and consistency under production constraints.

Preferred Qualifications

Preferred: experience evaluating LLM outputs for helpfulness, correctness, and safety; background in content safety labeling or policy-based annotation; exposure to taxonomy design and rubric development; experience with dataset analysis for training data quality issues; familiarity with common annotation platforms and workflow tooling; understanding of how labeled data connects to LLM training pipelines and model performance improvement.

Work Model, Schedule, and Location

This role is Remote and aligned to Atlanta talent, collaborating with distributed teams across the US. The position is FULL_TIME. You will operate in structured queues with clear throughput and quality targets, participating in calibration sessions to maintain consistent QA evaluation and annotation guidelines compliance.

Compensation

Salary Range: 63360–126720 USD per YEAR, depending on scope, domain complexity (NLP, computer vision annotation, content safety labeling), and demonstrated performance in training data quality and large language model evaluation workflows.

How to Apply on Rex.zone

Visit Rex.zone to apply for Senior AI Data Annotation jobs in Atlanta and explore related openings such as remote, contract, freelance, entry-level, and senior roles across NLP, computer vision, content safety, and LLM training pipelines. Ensure your profile highlights data labeling experience, QA evaluation ownership, prompt evaluation expertise, and RLHF familiarity.

Frequently Asked Questions

Q: What does a Senior AI Data Annotation Specialist do in practice?
You label and review training data used by AI systems, including data labeling for NLP and computer vision annotation, plus LLM evaluation tasks such as prompt evaluation and RLHF preference ranking. You also run QA evaluation and audits to maintain training data quality and annotation guidelines compliance.
Q: Is this role Remote and full-time?
Yes. Remote Type is Remote and Employment Type is FULL_TIME. The workflows are designed for distributed teams with quality calibration and structured QA evaluation.
Q: How is RLHF related to data annotation?
RLHF depends on human feedback data such as rankings, ratings, and critiques of model outputs. Senior annotators follow rubrics, perform prompt evaluation, and produce preference data that improves instruction-following and overall model performance improvement.
Q: What domains are covered by these Senior AI Data Annotation jobs in Atlanta?
Common domains include NLP annotation (named entity recognition, classification), computer vision annotation (bounding boxes, polygons, keypoints), content safety labeling, and large language model evaluation for LLM training pipelines.
Q: Do you offer contract or freelance roles too?
This page lists a FULL_TIME role, but Rex.zone often includes remote, contract, and freelance data labeling and evaluation openings depending on project needs and client timelines.
Q: What skills should I highlight to match this job intent?
Highlight AI data annotation, data labeling, RLHF, prompt evaluation, QA evaluation, LLM evaluation, NLP annotation, named entity recognition, computer vision annotation, content safety labeling, annotation guidelines compliance, and training data quality.

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