Phyllis W.

Key Skills

Software

CVAT

Scale AI

Labelbox

AWS SageMaker

SuperAnnotate

Top Subject Matter

Generative AI/ML Training-RLHF,Prompt writing,Response Quality evaluation &Creative writing for AI fine-Tuning

Health and Medical AI-Medical record/clinical notes annotation,symptom and diagnosis text classification and medical image labelling.

Autonomous Vehicle & Robotics-Bounding box labelling on images/videos,Object detection and tracking

Top Data Types

Image

Video

Audio

Top Task Types

Bounding Box

Polygon

Segmentation

Classification

Object Detection

Text Generation

Text Summarization

Red Teaming

Data Collection

Freelancer Overview

PROFESSIONAL SUMMARY Detail-oriented Data Annotation Specialist with 4+ years of hands-on experience labeling and enriching training datasets across image, video, text, audio, and medical modalities. Proven track record of delivering high-accuracy annotations that power production-ready AI and machine learning models. Proficient with industry-leading annotation platforms including Labelbox, Scale AI, CVAT, and Ango Hub. Strong understanding of supervised learning pipelines, quality assurance workflows, and AI/ML data enrichment processes. Adept at working in remote, cross-cultural, and interdisciplinary environments. Committed to ethical AI development and consistently exceeds quality benchmarks in high-volume annotation projects. WORK EXPERIENCE Senior Data Annotation Specialist | Freelance / Remote AI Projects Jan 2022 – Present Lead a team of 12 annotators on a large-scale computer vision project, achieving 98.5% annotation accuracy across 2M+ image assets for an autonomous vehicle client. Performed semantic segmentation, bounding box, and polygon annotation on dashcam video datasets used to train object detection models. Developed and maintained annotation guidelines and quality rubrics, reducing re-annotation rates by 35%. Conducted QA reviews and inter-annotator agreement (IAA) scoring

Labeling Experience

Senior Data Anotation Specialist

ImageBounding Box

Lead a team of 12 annotators on a large scale computer vision project ,achieving 98.5% annotation accuracy across 2M+ image assets for an automation vehicle client.

2022 - 2026

Education

Work History