Sarah M.

Key Skills

Software

Labelbox

Scale AI

Top Subject Matter

NLP dataset annotation for machine learning and model evaluation

Image recognition dataset annotation and QA

Legal Services & Contract Review

Top Data Types

Text

Image

Document

Top Task Types

Entity (NER) Classification

Object Detection

Freelancer Overview

Independent / Freelance AI data annotation and model QA (NLP). Brings 19+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox and Scale AI. Education includes Bachelor of Arts, Bellarmine University (2016). AI-training focus includes data types such as Text and Image and labeling workflows including Entity (NER) Classification and Object Detection.

Labeling Experience

Independent / Freelance AI data annotation and model QA (image recognition)

LabelboxImageObject Detection

Independently supported image-recognition dataset annotation for machine learning training pipelines. Ensured labeled data met quality requirements through review and correction workflows. Contributed to evaluation readiness of datasets used to train or improve AI models. • Annotated datasets for image recognition projects. • Performed dataset quality checks to improve labeling reliability. • Iterated based on QA findings to enhance annotation usefulness. • Supported model evaluation with better-prepared labeled inputs.

2016 - 2025

Independent / Freelance AI data annotation and model QA (NLP)

LabelboxTextEntity (NER) Classification

Independently annotated NLP datasets for training and evaluation purposes, focusing on creating high-quality labeled examples. Performed iterative checks to ensure label consistency and improve downstream model performance. Used annotation workflows to refine dataset quality and reliability for AI use cases. • Labeled text data for NLP and related machine learning tasks. • Conducted quality assurance on labeled datasets. • Provided feedback on model outputs to refine automated predictions. • Improved model accuracy by 15–20% through labeling QA.

2016 - 2025

Education

Work History