Jennifer Green

Key Skills

Software

Remotasks

Appen

Top Subject Matter

General AI/ML

AI Response Generation

General AI Dataset Labeling

Top Data Types

Text

Audio

Top Task Types

Classification

Freelancer Overview

Freelance AI Data Annotator & Evaluator. With less than one year of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Data Annotation, Prompt evaluation, AI training support, and Research and Communication Education includes a Bachelor of Science from Rivers State University (2019). AI-training focus includes data types such as Text and labeling workflows, including Evaluation, Rating, and Classification.

Labeling Experience

Freelance AI Data Annotator & Evaluator

RemotasksText

As a Freelance AI Data Annotator and Evaluator, I annotated and labeled large-scale datasets across text, audio, and images for supervised learning pipelines. I ranked and compared AI output responses following strict rubrics, ensuring accuracy and relevance. My role required maintaining consistent quality on high-volume projects and writing clear rationales to enhance dataset reliability. • Annotated text, audio, and image datasets following project guidelines • Flagged edge cases and ambiguities to improve annotation standards • Improved data quality by providing rationale for labeling decisions • Supported continuous improvement of AI models and datasets

As a Freelance AI Data Annotator and Evaluator, I annotated and labeled large-scale datasets across text, audio, and images for supervised learning pipelines. I ranked and compared AI output responses following strict rubrics, ensuring accuracy and relevance. My role required maintaining consistent quality on high-volume projects and writing clear rationales to enhance dataset reliability. • Annotated text, audio, and image datasets following project guidelines • Flagged edge cases and ambiguities to improve annotation standards • Improved data quality by providing rationale for labeling decisions • Supported continuous improvement of AI models and datasets

2025 - Present

AI Quality Assurance & Content Review Project Contributor

AppenText

During the AI Quality Assurance & Content Review Project, I reviewed AI-generated content for compliance with established quality standards. I identified errors and inconsistencies, supporting enhanced system performance. My work was pivotal for quality assurance protocols in AI outputs. • Checked AI output for bias, hallucination, and low-quality responses • Documented errors and inconsistency findings for improvement • Offered feedback to raise overall content reliability • Supported quality benchmarking for AI model releases

During the AI Quality Assurance & Content Review Project, I reviewed AI-generated content for compliance with established quality standards. I identified errors and inconsistencies, supporting enhanced system performance. My work was pivotal for quality assurance protocols in AI outputs. • Checked AI output for bias, hallucination, and low-quality responses • Documented errors and inconsistency findings for improvement • Offered feedback to raise overall content reliability • Supported quality benchmarking for AI model releases

2024 - Present

Data Annotation & Content Labeling Project Contributor

RemotasksTextClassification

For the Data Annotation & Content Labeling Project, I was responsible for annotating datasets in various formats to support machine learning model training. I ensured strict adherence to labeling guidelines for consistency and quality. My efforts contributed to the improved reliability and utility of training data for AI systems. • Annotated and categorized content in multiple data types • Maintained documentation and ensured label quality • Followed complex annotation protocols across projects • Improved overall dataset consistency and integrity

For the Data Annotation & Content Labeling Project, I was responsible for annotating datasets in various formats to support machine learning model training. I ensured strict adherence to labeling guidelines for consistency and quality. My efforts contributed to the improved reliability and utility of training data for AI systems. • Annotated and categorized content in multiple data types • Maintained documentation and ensured label quality • Followed complex annotation protocols across projects • Improved overall dataset consistency and integrity

2024 - Present

AI Response Evaluation & Ranking Project Contributor

AppenText

On the AI Response Evaluation & Ranking Project, I evaluated AI-generated responses for accuracy, relevance, and compliance with instructions. I provided structured reasoning to support higher response quality and dataset consistency. This process contributed to refining AI evaluation datasets and improved model outputs. • Assessed AI responses using rubric-based systems • Delivered structured feedback to inform dataset improvements • Collaborated on projects for AI evaluation and response ranking • Enhanced quality and utility of training datasets

On the AI Response Evaluation & Ranking Project, I evaluated AI-generated responses for accuracy, relevance, and compliance with instructions. I provided structured reasoning to support higher response quality and dataset consistency. This process contributed to refining AI evaluation datasets and improved model outputs. • Assessed AI responses using rubric-based systems • Delivered structured feedback to inform dataset improvements • Collaborated on projects for AI evaluation and response ranking • Enhanced quality and utility of training datasets

2024 - Present

Education

Work History