Moores Michael

Key Skills

Software

Scale AI

Label Studio

Mercor

Top Subject Matter

No subject matter listed

Top Data Types

Text

Image

Video

Audio

Top Label Types

Bounding Box

Segmentation

Classification

RLHF

Transcription

Translation Localization

Emotion Recognition

Object Detection

Action Recognition

Tracking

Audio Recording

Evaluation Rating

Data Collection

Freelancer Overview

I am an experienced AI training data and annotation specialist with over 5 years of hands-on work across leading platforms such as Outlier AI, Scale AI, and Appen. My background spans more than 8,500 high-complexity annotation and evaluation tasks with a 98.5% accuracy rating, including RLHF, supervised fine-tuning, and multimodal data labeling for text, image, audio, and video. I have deep expertise in domains like NLP (named entity recognition, sentiment analysis), computer vision (bounding boxes, semantic segmentation, lidar annotation), and business document evaluation for finance, legal, and medical data. My toolkit includes Labelbox, CVAT, Label Studio, and SageMaker Ground Truth, as well as Python, SQL, and advanced Excel for data workflows. I consistently deliver top-quality, bias-aware datasets and actionable feedback, supporting the development of enterprise-grade AI models for Fortune 500 clients. My approach combines technical precision, strong business acumen, and a commitment to continuous learning in the evolving AI landscape.

Labeling Experience

AI Training & Data Annotation Specialist

MercorAudioClassificationEvaluation Rating

Performed advanced data labeling and evaluation tasks across multimodal datasets to support AI model training and fine-tuning. Focused on high-accuracy text classification, image bounding box creation, and audio transcription for enterprise-level LLM and computer vision systems. Conducted quality assurance reviews, verified dataset integrity, and provided structured feedback to improve model performance. Collaborated with engineers and researchers to refine annotation workflows and maintain a 98%+ accuracy rate across diverse labeling projects.

Performed advanced data labeling and evaluation tasks across multimodal datasets to support AI model training and fine-tuning. Focused on high-accuracy text classification, image bounding box creation, and audio transcription for enterprise-level LLM and computer vision systems. Conducted quality assurance reviews, verified dataset integrity, and provided structured feedback to improve model performance. Collaborated with engineers and researchers to refine annotation workflows and maintain a 98%+ accuracy rate across diverse labeling projects.

2024

Senior Data Annotation & AI Evaluation Specialist

Scale AITextBounding BoxSegmentation

Performed over 8,500 complex data labeling and evaluation tasks across text, image, audio, and video modalities for AI model training and fine-tuning. Specialized in RLHF, supervised fine-tuning (SFT), and multimodal annotation for enterprise-level AI systems. Delivered 98.5% accuracy across 10+ global platforms including Scale AI, Appen, and Outlier. Created and reviewed datasets for autonomous driving, conversational AI, and document intelligence. Conducted quality assurance reviews, trained new annotators, and developed annotation guidelines improving consistency by 22%. Recognized as a top 5% global contributor for precision, speed, and adherence to annotation protocols.

Performed over 8,500 complex data labeling and evaluation tasks across text, image, audio, and video modalities for AI model training and fine-tuning. Specialized in RLHF, supervised fine-tuning (SFT), and multimodal annotation for enterprise-level AI systems. Delivered 98.5% accuracy across 10+ global platforms including Scale AI, Appen, and Outlier. Created and reviewed datasets for autonomous driving, conversational AI, and document intelligence. Conducted quality assurance reviews, trained new annotators, and developed annotation guidelines improving consistency by 22%. Recognized as a top 5% global contributor for precision, speed, and adherence to annotation protocols.

2017

Senior RLHF & Data Annotation Specialist

Label StudioImageBounding BoxSegmentation

Executed 5,500+ advanced data annotation tasks for large-scale machine learning projects, including text classification, named entity recognition, and sentiment analysis. Conducted RLHF evaluations ranking AI responses by helpfulness, honesty, and safety. Delivered consistent 99% inter-annotator agreement across benchmark datasets. Created annotation guidelines that improved labeling accuracy by 18% and trained new annotators on quality standards. Collaborated with ML engineers to refine dataset quality and enhance model fine-tuning outcomes.

Executed 5,500+ advanced data annotation tasks for large-scale machine learning projects, including text classification, named entity recognition, and sentiment analysis. Conducted RLHF evaluations ranking AI responses by helpfulness, honesty, and safety. Delivered consistent 99% inter-annotator agreement across benchmark datasets. Created annotation guidelines that improved labeling accuracy by 18% and trained new annotators on quality standards. Collaborated with ML engineers to refine dataset quality and enhance model fine-tuning outcomes.

2022 - 2023

AI Evaluator & Linguistic Data Annotator

Label StudioVideoEmotion RecognitionObject Detection

Evaluated 3,000+ AI assistant and search engine responses for accuracy, relevance, and user intent alignment. Conducted linguistic annotation tasks including part-of-speech tagging, dependency parsing, and semantic role labeling. Performed multilingual content evaluation across English and Swahili datasets with 98% inter-annotator agreement. Delivered consistent top-quality results under strict deadlines and contributed to improving AI model understanding of natural language and voice commands.

Evaluated 3,000+ AI assistant and search engine responses for accuracy, relevance, and user intent alignment. Conducted linguistic annotation tasks including part-of-speech tagging, dependency parsing, and semantic role labeling. Performed multilingual content evaluation across English and Swahili datasets with 98% inter-annotator agreement. Delivered consistent top-quality results under strict deadlines and contributed to improving AI model understanding of natural language and voice commands.

2021 - 2022

Education

Work History