Taylor L.

Key Skills

Software

Other

Scale AI

Top Subject Matter

Physics / Nuclear fuel materials

microscopy image analysis

Multi-industry AI/ML (computer vision and NLP)

Top Data Types

Image

Text

3D Sensor

Top Task Types

Segmentation

Polygon

Cuboid

Freelancer Overview

Physics Doctoral Student - Graduate Research Assistant (AI-based image annotation datasets for materials defects). Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and N. Education includes Doctor of Philosophy, North Carolina State University (2026) and Bachelor of Science, University of North Carolina at Charlotte (2025). AI-training focus includes data types such as Image, Text, and 3D Sensor and labeling workflows including Segmentation, Polygon, and Evaluation.

Labeling Experience

Physics Doctoral Student - Graduate Research Assistant (AI-based image annotation datasets for materials defects)

ImageSegmentation

Developed AI-based image annotation workflows to automate measurement and classification of materials defects in microscopy imagery for machine learning. Labeled and annotated thousands of microscopy images to support training of materials science ML models while applying quality control procedures to ensure accuracy and consistency. Focused on improving annotation efficiency and correctness using research-driven guidelines and verification steps. • Annotated microscopy images for defect classification • Implemented quality control for labeled research data • Built AI image annotation tools for measurement and categorization • Prepared labeled datasets for materials analysis

2026 - Present

Freelance Data Annotation Specialist (self-employed)

ImagePolygon

Provided professional data annotation services to multiple AI/ML clients across different industries using computer vision labeling techniques. Completed image labeling work including bounding boxes, polygon segmentation, and keypoint labeling for training vision systems. Also performed natural language labeling tasks including text classification and sentiment analysis for NLP applications while maintaining guideline-based quality. • Bounding box, polygon segmentation, and keypoint labeling for CV projects • Text classification and sentiment analysis labeling for NLP • Maintained 98%+ accuracy by following annotation guidelines • Completed 1,000+ monthly annotation tasks under tight deadlines

2024 - Present

Undergraduate Researcher (UNC Charlotte)

ImageSegmentation

Contributed to advanced materials research that required extensive image data annotation and labeling for machine learning applications. Gained hands-on experience with microscopy and materials characterization data, including using image annotation workflows to prepare training data. Helped develop foundational AI-based image analysis tools by combining domain understanding with dataset labeling expertise. • Annotated microscopy/materials characterization images for ML • Practiced end-to-end microscopy annotation workflows • Supported creation of AI-based image analysis tools • Focused on producing research-ready labeled datasets

2024 - 2025

Data Annotation Specialist, Handshake

OtherText

Labeled and categorized data for Handshake’s career networking and recruitment platform to support search and recommendation improvements. Annotated job postings, company profiles, and user-generated content, partnering with ML engineers to refine annotation guidelines based on model performance feedback. Performed quality assurance reviews on labeling output to keep results consistent across the annotation team. Achieved top-10% performance based on accuracy and productivity metrics. • Labeled job postings, company profiles, and user-generated content • Conducted QA reviews for consistency across the labeling team • Collaborated with ML engineers to iterate annotation guidelines • Delivered high-accuracy, high-productivity performance ratings

2023 - 2024

Data Labeling Annotator, Scale AI

Scale AI3D SensorCuboid

Performed high-volume data annotation for autonomous vehicle, robotics, and computer vision AI training datasets. Completed 2D and 3D bounding box annotation, semantic segmentation, and object tracking labels to support self-driving applications. Labeled sensor fusion data including LiDAR point clouds, camera images, and radar signals with high precision while maintaining strong quality metrics. Completed advanced certifications for complex annotation tasks such as 3D cuboid labeling and instance segmentation. • 2D/3D bounding boxes for autonomous vehicle datasets • Semantic segmentation and object tracking annotations • Sensor fusion labeling across LiDAR, camera, and radar • Maintained 97%+ quality on 10,000+ tasks

2022 - 2023

Education

Work History