G C

Key Skills

Software

Appen

Lionbridge

Top Subject Matter

Large Language Models

Nlp Domain Expertise

Rlhf Domain Expertise

Top Data Types

Text

Image

Document

Top Task Types

RLHF

Entity (NER) Classification

Classification

Freelancer Overview

Senior AI Data Trainer. Brings 11+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Internal, Proprietary Tooling, and Appen. Education includes High School Diploma, Pulaski County High School. AI-training focus includes data types such as Text and labeling workflows including RLHF, Entity (NER) Classification, and Classification.

Labeling Experience

Senior AI Data Trainer

TextRLHF

Led a distributed annotation team for RLHF and instruction-tuning projects aimed at large language models. Designed annotation taxonomies and labeling guidelines for topics such as sentiment analysis and factuality. Collaborated with ML engineers to evaluate and iterate on model outputs for improved accuracy and lower error rates. • Oversaw 2M+ labeled examples for sentiment analysis and response quality scoring. • Developed a 40-page style guide for onboarding and training annotators. • Built QA workflows and calibration protocols, lowering error rates by 34%. • Maintained over 97% inter-annotator agreement throughout projects.

Led a distributed annotation team for RLHF and instruction-tuning projects aimed at large language models. Designed annotation taxonomies and labeling guidelines for topics such as sentiment analysis and factuality. Collaborated with ML engineers to evaluate and iterate on model outputs for improved accuracy and lower error rates. • Oversaw 2M+ labeled examples for sentiment analysis and response quality scoring. • Developed a 40-page style guide for onboarding and training annotators. • Built QA workflows and calibration protocols, lowering error rates by 34%. • Maintained over 97% inter-annotator agreement throughout projects.

2021 - Present

AI Annotation Specialist

AppenTextEntity Ner Classification

Performed high-volume labeling for text, image, and audio datasets supporting conversational AI and computer vision. Specialized in named entity recognition, intent classification, and dialogue quality rating for AI training. Contributed to structured feedback for improved guideline clarity and team-wide labeling consistency. • Ranked in the 98th percentile across 30+ concurrent annotation projects. • Selected for expert-level, high-sensitivity annotation assignments. • Labeled data for image and audio datasets used in voice assistant training systems. • Worked with ambiguous labeling tasks requiring expert judgment.

Performed high-volume labeling for text, image, and audio datasets supporting conversational AI and computer vision. Specialized in named entity recognition, intent classification, and dialogue quality rating for AI training. Contributed to structured feedback for improved guideline clarity and team-wide labeling consistency. • Ranked in the 98th percentile across 30+ concurrent annotation projects. • Selected for expert-level, high-sensitivity annotation assignments. • Labeled data for image and audio datasets used in voice assistant training systems. • Worked with ambiguous labeling tasks requiring expert judgment.

2018 - 2020

Data Annotation Reviewer

LionbridgeTextClassification

Reviewed and validated text annotations for multilingual NLP datasets used in machine translation and chatbot training. Flagged systemic annotation inconsistencies across crowd-sourced batches to improve data quality. Collaborated in developing quizzes to calibrate and certify new annotation contributors. • Processed and reviewed more than 500,000 annotation tasks. • Supported text classification, relevance rating, and entity tagging workflows. • Enhanced annotation quality through detailed error identification procedures. • Ensured rigorous QA and onboarding standards on NLP projects.

Reviewed and validated text annotations for multilingual NLP datasets used in machine translation and chatbot training. Flagged systemic annotation inconsistencies across crowd-sourced batches to improve data quality. Collaborated in developing quizzes to calibrate and certify new annotation contributors. • Processed and reviewed more than 500,000 annotation tasks. • Supported text classification, relevance rating, and entity tagging workflows. • Enhanced annotation quality through detailed error identification procedures. • Ensured rigorous QA and onboarding standards on NLP projects.

2016 - 2018

Education

Work History