Zhang W.

Key Skills

Software

Labelbox

Top Subject Matter

Autonomous Vehicles

Computer Vision

Nlp Domain Expertise

Top Data Types

Image

Text

Document

Top Task Types

Segmentation

RLHF

Freelancer Overview

Senior Data Annotation Specialist. Brings 6+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Labelbox and Amazon SageMaker. Education includes Associate Degree, Sichuan Radio and TV University (2012). AI-training focus includes data types such as Image and Text and labeling workflows including Segmentation and RLHF.

Labeling Experience

Senior Data Annotation Specialist

LabelboxImageSegmentation

As a Senior Data Annotation Specialist at Apex AI Solutions, I managed full annotation lifecycles for autonomous vehicle projects. My focus was on Computer Vision data, such as semantic segmentation and 3D LiDAR Point Cloud annotations, using AI-assisted tools. I developed and implemented NLP data labeling protocols and optimized annotation workflows with internal scripting and custom tools. • Led annotation projects for semantic segmentation on images and 3D LiDAR Point Cloud data. • Developed protocols for NLP labeling, focusing on Named Entity Classification and LLM fine-tuning. • Used Labelbox, Snorkel, Prodigy, Amazon SageMaker Ground Truth, and custom tools for annotation. • Mentored annotation teams to ensure high standards and project scalability.

2022 - 2022

Lead Data Annotation & RLHF Specialist

TextRLHF

At DataForge Labs, I led data annotation and training projects in Natural Language Processing and Computer Vision. I implemented robust data quality frameworks and RLHF for optimizing annotation accuracy and feedback loops. I also mentored teams, ensuring quality control and scalable, reliable solutions. • Led and mentored teams in NLP and CV annotation projects. • Implemented rigorous data quality control (QA) and RLHF frameworks in annotation workflows. • Used Amazon SageMaker Ground Truth, Labelbox, Snorkel, and custom internal tools. • Achieved 99%+ accuracy in team annotation performance at scale.

Not specified

Education

Work History