Jackson Makappy

Key Skills

Software

AWS SageMaker

CVAT

Dataloop

Diffgram

Labelbox

Mercor

Roboflow

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Text

Top Task Types

Bounding Box

Object Detection

Polygon

Segmentation

Text Generation

Freelancer Overview

I am an experienced AI Data Labeling and Training Specialist with over 4 years of hands-on expertise in delivering high-quality annotations and evaluations for image, text, and code-based AI systems. My work focuses on building reliable datasets that power computer vision models, natural language processing systems, and LLM fine-tuning initiatives. I have contributed to AI development projects for global clients through platforms such as Scale AI, Mercor, Appen, and CloudFactory, where I ensured data integrity, consistency, and accuracy across diverse labeling tasks including object detection, segmentation, text summarization, and prompt-response alignment. My background in Artificial Intelligence and Data Science (M.Sc.) strengthens my ability to understand machine learning pipelines and optimize data for model training. I take pride in combining technical precision with ethical AI standards to advance fair, transparent, and reliable model performance.

Labeling Experience

Autonomous Vehicle Image Annotation for Computer Vision Model Training

CVATImageBounding BoxPolygon

Contributed to a large-scale autonomous vehicle AI training initiative focused on improving the accuracy of object detection and lane recognition systems. My role involved annotating high-resolution street and traffic imagery using bounding boxes, polygons, and segmentation tools to identify pedestrians, vehicles, and environmental objects. I maintained an average annotation accuracy rate of 98%, meeting stringent project standards and QA benchmarks. Collaborated with cross-functional teams to review and validate image data for consistency and quality assurance. The dataset directly supported real-time perception algorithms for self-driving systems used in model evaluation and simulation testing.

Contributed to a large-scale autonomous vehicle AI training initiative focused on improving the accuracy of object detection and lane recognition systems. My role involved annotating high-resolution street and traffic imagery using bounding boxes, polygons, and segmentation tools to identify pedestrians, vehicles, and environmental objects. I maintained an average annotation accuracy rate of 98%, meeting stringent project standards and QA benchmarks. Collaborated with cross-functional teams to review and validate image data for consistency and quality assurance. The dataset directly supported real-time perception algorithms for self-driving systems used in model evaluation and simulation testing.

2022 - 2023

Education

Work History