J B.

Key Skills

Software

Scale AI

Other

Anno-Mage

Appen

Axiom AI

Data Annotation Tech

HiveMind

Mercor

Top Subject Matter

Autonomous Vehicles

Retail Domain Expertise

Satellite Imagery

Top Data Types

Image

Text

Document

Top Task Types

Object Detection

Classification

Land Cover Classification

Freelancer Overview

Image Data Annotator & QA Reviewer ( Scale AI, Contract). Brings 4+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Scale AI, Internal, and Proprietary Tooling. Education includes Doctor of Philosophy, University of Washington (2021) and Bachelor of Science, Washington State University (2016). AI-training focus includes data types such as Image, Geospatial, and Tiled Imagery and labeling workflows including Object Detection, Classification, and Land Cover Classification.

Labeling Experience

Image Data Annotator & QA Reviewer ( Scale AI, Contract)

Scale AIImageObject Detection

I annotated large batches of images for computer vision datasets focusing on object detection, segmentation, and classification applications. My work emphasized high throughput with consistent accuracy, contributing to autonomous vehicle, retail, and satellite imagery projects. As a Tier 2 Quality Reviewer, I performed final-pass QA, adjudicated disputes, and refined annotation guidelines to improve team output. • Labeled 300–500 images/week using CVAT and Labelbox for multi-domain datasets • Maintained a 98.4% annotation accuracy rate verified by client audits • Reviewed and resolved annotation disputes in accordance with project style guides • Enhanced project label schemas by flagging ambiguous edge cases

2023 - Present

Postdoctoral Research Associate (Data Labeling, PNNL)

ImageClassification

I analyzed and annotated scientific imagery datasets such as electron microscopy and neutron scattering maps, meticulously identifying and labeling structural features. I created and maintained detailed written annotation protocols for use by myself and a team of graduate interns, ensuring consistency through inter-annotator agreement checks. Additionally, I collaborated with data scientists to provide labeled data for internal ML model training on material recognition. • Developed data preprocessing scripts (OpenCV, Pillow) for efficient manual and automated labeling • Regularly checked dataset consistency across annotators to improve reliability • Supplied high-quality labeled image sets for ML microstructure classification • Authored and updated annotation guidelines and taxonomies used by the group

2021 - Present

Graduate Research Assistant (Data Annotation, UW Physics)

ImageClassification

I processed and visually annotated images created from quantum simulation outputs, manually classifying thousands of image snapshots by phase for ML experiments. My role involved pipeline development for consistent rendering, tagging, and exporting of simulation frames. Close attention to dataset quality and documentation of labeling edge cases helped standardize data for the group and collaborating teams. • Visually classified 10,000+ quantum simulation images for supervised ML • Designed and implemented a labeling/export pipeline in Python/Matplotlib • Performed systematic quality checks and resolved labeling discrepancies • Documented edge cases and labeling practices in shared internal resources

2016 - 2021

Physics & Data Visualization Intern (WA Dept. of Ecology)

OtherLand Cover Classification

I manually reviewed and annotated satellite and aerial image tiles for land-use classification to contribute to a large-scale environmental monitoring dataset. I consistently applied labeling protocols across multiple geographic and image-based categories using GIS-integrated tools. My work helped standardize a comprehensive dataset for state-wide environmental analysis. • Labeled thousands of image tiles by land use category (water, vegetation, impervious) • Applied GIS protocols for consistent annotation • Ensured uniformity and accuracy across large-scale geospatial data • Contributed to a public environmental mapping resource

2015 - 2015

Education

Work History