Sullins Jeff

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Top Task Types

Bounding Box

Point Key Point

Segmentation

Classification

Tracking

Emotion Recognition

Freelancer Overview

I am an experienced AI Training Specialist with three years of hands-on work in data labeling and annotation for image, video, and audio datasets. My expertise spans computer vision workflows, including object detection, semantic segmentation, and multi-object tracking, as well as audio labeling and transcription alignment. I am highly proficient with industry-standard tools such as Labelbox, YOLO, CVAT, Supervisely, and VGG Image Annotator, and have a strong background in maintaining dataset quality through detailed QA processes. By collaborating closely with AI engineers and data scientists, I have helped refine annotation guidelines and improved dataset accuracy by 15%. I am committed to delivering high-quality, consistent training data that drives better model performance, and I excel at adapting quickly to new tools and meeting tight project deadlines in remote environments.

Labeling Experience

AI Image & Video Annotation Specialist

LabelboxVideoBounding BoxPoint Key Point

Led comprehensive video annotation for AI/ML training datasets in computer vision projects. Responsibilities included: Annotating videos with object detection and multi-object tracking using bounding boxes and polygons. Performing frame-by-frame labeling to capture precise object movements and interactions. Ensuring annotation consistency across frames for accurate model training. Collaborating with data scientists to define and refine labeling guidelines specific to project goals. Conducting quality assurance checks to maintain high accuracy and dataset reliability. Project scale: Processed hundreds of hours of video footage, tracking multiple objects per frame, improving model performance and detection accuracy.

Led comprehensive video annotation for AI/ML training datasets in computer vision projects. Responsibilities included: Annotating videos with object detection and multi-object tracking using bounding boxes and polygons. Performing frame-by-frame labeling to capture precise object movements and interactions. Ensuring annotation consistency across frames for accurate model training. Collaborating with data scientists to define and refine labeling guidelines specific to project goals. Conducting quality assurance checks to maintain high accuracy and dataset reliability. Project scale: Processed hundreds of hours of video footage, tracking multiple objects per frame, improving model performance and detection accuracy.

2024

AI Image & Video Annotation Specialist

LabelboxImageBounding BoxPoint Key Point

Managed end-to-end data labeling and annotation for multi-modal AI datasets including images, videos, and audio. Tasks included: Annotating images with bounding boxes, polygons, and semantic segmentation for object detection models. Performing frame-by-frame video tracking of multiple objects using YOLO and CVAT workflows. Conducting audio transcription, segmentation, and alignment for speech datasets. Implementing rigorous quality assurance processes to ensure high dataset accuracy and consistency. Collaborating with AI engineers to refine annotation guidelines and maintain dataset integrity. Project scale: Annotated and validated thousands of images, hundreds of hours of video, and hundreds of hours of audio recordings. Achieved a 15% improvement in dataset accuracy through structured QA validation.

Managed end-to-end data labeling and annotation for multi-modal AI datasets including images, videos, and audio. Tasks included: Annotating images with bounding boxes, polygons, and semantic segmentation for object detection models. Performing frame-by-frame video tracking of multiple objects using YOLO and CVAT workflows. Conducting audio transcription, segmentation, and alignment for speech datasets. Implementing rigorous quality assurance processes to ensure high dataset accuracy and consistency. Collaborating with AI engineers to refine annotation guidelines and maintain dataset integrity. Project scale: Annotated and validated thousands of images, hundreds of hours of video, and hundreds of hours of audio recordings. Achieved a 15% improvement in dataset accuracy through structured QA validation.

2021 - 2024

Education

Work History