Kenneth M.

Key Skills

Software

Anno-Mage

Argilla

Clickworker

Dataloop

Label Studio

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Image

Top Task Types

Audio Recording

Classification

Cuboid

Data Collection

Freelancer Overview

I am an experienced Data Annotation and Labeling Specialist with over three years of expertise in training AI models through high-quality data labeling across text, image, speech, and video datasets. I have worked on projects involving Natural Language Processing (NLP), Computer Vision, and Speech Recognition, ensuring precise and efficient data processing to enhance AI model accuracy. My proficiency in industry-standard annotation tools like Labelbox, CVAT, and Amazon SageMaker Ground Truth, combined with my keen attention to detail, enables me to deliver consistently high-quality datasets. Beyond annotation, I specialize in dataset validation, AI model evaluation, and quality assurance, ensuring labeled data meets industry standards. I have collaborated closely with AI engineers and data scientists, providing actionable insights to optimize machine learning workflows. My ability to identify and correct inconsistencies in AI-generated data, coupled with my strong analytical skills, allows me to contribute effectively to AI development. Passionate about improving AI performance through accurate data annotation, I am committed to continuous learning and staying updated with the latest advancements in AI training data.

Labeling Experience

Data annotation

Label StudioImageBounding Box

Labeled images at Remotasks for autonomous vehicle training, focusing on critical elements such as traffic signs, pedestrians, road markings, and vehicles. Utilized precise annotation techniques, including bounding boxes and semantic segmentation, to enhance AI perception models. Ensured high accuracy and consistency in labeling to improve object detection and road scene understanding for self-driving systems.

Labeled images at Remotasks for autonomous vehicle training, focusing on critical elements such as traffic signs, pedestrians, road markings, and vehicles. Utilized precise annotation techniques, including bounding boxes and semantic segmentation, to enhance AI perception models. Ensured high accuracy and consistency in labeling to improve object detection and road scene understanding for self-driving systems.

2022 - 2023

Education

Work History