Lauren Clack

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Top Label Types

Bounding Box

Point Key Point

Segmentation

Tracking

Freelancer Overview

I am an experienced AI Training Specialist with four years of hands-on expertise in data labeling and annotation for machine learning projects spanning image, video, audio, and text data. My background includes working extensively with computer vision tasks such as bounding box and polygon segmentation, semantic and instance segmentation, keypoint annotation, and multi-object tracking, as well as NLP-focused annotation like Named Entity Recognition, text classification, and sentiment analysis. I am skilled in using industry-standard annotation tools including Labelbox, CVAT, Supervisely, VGG Image Annotator, and Amazon SageMaker Ground Truth, and I am comfortable preparing datasets in YOLO format for object detection. My approach emphasizes data quality assurance and consistent labeling standards, ensuring that datasets are accurate and ready to support high-performance model training. I have collaborated closely with machine learning engineers to refine annotation guidelines and improve dataset outcomes, and I am adept at managing large-scale data projects with attention to detail and confidentiality. My technical foundation in computer science and machine learning, combined with strong analytical and time management skills, enables me to deliver reliable and precise training data for a wide range of AI applications.

Labeling Experience

Autonomous Vehicle Object Detection & Multi-Object Tracking Dataset

LabelboxVideoBounding BoxPoint Key Point

Project Description Contributed to a large-scale autonomous vehicle training dataset focused on object detection and multi-object tracking in urban environments. Annotated over 120,000+ images and 3,500+ video sequences containing vehicles, pedestrians, cyclists, traffic signs, and road infrastructure. Key responsibilities included: Drawing high-precision bounding boxes for moving and stationary objects Polygon segmentation for complex objects and occlusions Frame-by-frame video annotation with multi-object tracking IDs Object classification (vehicle types, pedestrian states, traffic signals) YOLO-format dataset preparation for training object detection models Validating annotations for consistency and accuracy Maintained annotation accuracy above 98% through structured QA processes, including peer reviews and guideline adherence. Followed strict labeling ontologies and version-controlled annotation standards to ensure dataset uniformity across the team.

Project Description Contributed to a large-scale autonomous vehicle training dataset focused on object detection and multi-object tracking in urban environments. Annotated over 120,000+ images and 3,500+ video sequences containing vehicles, pedestrians, cyclists, traffic signs, and road infrastructure. Key responsibilities included: Drawing high-precision bounding boxes for moving and stationary objects Polygon segmentation for complex objects and occlusions Frame-by-frame video annotation with multi-object tracking IDs Object classification (vehicle types, pedestrian states, traffic signals) YOLO-format dataset preparation for training object detection models Validating annotations for consistency and accuracy Maintained annotation accuracy above 98% through structured QA processes, including peer reviews and guideline adherence. Followed strict labeling ontologies and version-controlled annotation standards to ensure dataset uniformity across the team.

2024 - 2024

Autonomous Vehicle Object Detection & Multi-Object Tracking Dataset

LabelboxImageBounding BoxPoint Key Point

Project Description Contributed to a large-scale autonomous vehicle training dataset focused on object detection and multi-object tracking in urban environments. Annotated over 120,000+ images and 3,500+ video sequences containing vehicles, pedestrians, cyclists, traffic signs, and road infrastructure. Key responsibilities included: Drawing high-precision bounding boxes for moving and stationary objects Polygon segmentation for complex objects and occlusions Frame-by-frame video annotation with multi-object tracking IDs Object classification (vehicle types, pedestrian states, traffic signals) YOLO-format dataset preparation for training object detection models Validating annotations for consistency and accuracy Maintained annotation accuracy above 98% through structured QA processes, including peer reviews and guideline adherence. Followed strict labeling ontologies and version-controlled annotation standards to ensure dataset uniformity across the team.

Project Description Contributed to a large-scale autonomous vehicle training dataset focused on object detection and multi-object tracking in urban environments. Annotated over 120,000+ images and 3,500+ video sequences containing vehicles, pedestrians, cyclists, traffic signs, and road infrastructure. Key responsibilities included: Drawing high-precision bounding boxes for moving and stationary objects Polygon segmentation for complex objects and occlusions Frame-by-frame video annotation with multi-object tracking IDs Object classification (vehicle types, pedestrian states, traffic signals) YOLO-format dataset preparation for training object detection models Validating annotations for consistency and accuracy Maintained annotation accuracy above 98% through structured QA processes, including peer reviews and guideline adherence. Followed strict labeling ontologies and version-controlled annotation standards to ensure dataset uniformity across the team.

2022 - 2024

Education

Work History