Musa Paul

Key Skills

Software

Appen

Clickworker

CloudFactory

CrowdSource

CVAT

Dataloop

Encord

Hasty

Humanatic

iMerit

Img Lab

Labelbox

Label Studio

Mighty AI

Mindrift

Remotasks

Sama

Scale AI

Toloka

V7 Labs

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Image

Video

Top Label Types

Bounding Box

Classification

Cuboid

Object Detection

Segmentation

Freelancer Overview

I have extensive experience in data labeling and AI training data, with a strong track record of managing and delivering high-quality annotation projects across 2D and 3D domains. My expertise spans a wide range of annotation tools including Basic AI, V7 Labs, Segment AI, Supervisely, CVAT, and Scale AI. I’ve successfully trained and supervised large teams—over 130 taskers—in the execution of complex labeling tasks, ensuring accuracy, consistency, and alignment with evolving client requirements. What sets me apart is my deep understanding of quality control frameworks, delivery KPIs, and annotation processes, gained through hands-on project management and team leadership roles. I've led training programs on key aspects such as project delivery, risk management, quality playbooks, calibration sessions, and annotation tool best practices, even under challenging conditions like poor connectivity. My ability to balance operational efficiency with precision in labeled datasets has consistently contributed to the success of computer vision and machine learning initiatives.

Labeling Experience

data labeling services. 2d and 3d annotation

Scale AI3D SensorBounding BoxPoint Key Point

One of the most impactful projects I led was a large-scale autonomous driving annotation initiative involving both 2D and 3D data. The project required labeling camera images and LiDAR point clouds with bounding boxes, polygons, lane markings, and 3D cuboids to train self-driving vehicle perception models. Using tools like Scale AI and Supervisely, I managed a team of over 100 annotators and implemented rigorous quality control workflows to ensure high precision and consistency across millions of frames. I also conducted hands-on training and calibration sessions to align team output with client standards, ultimately contributing to the development of safer and more accurate autonomous navigation systems.

One of the most impactful projects I led was a large-scale autonomous driving annotation initiative involving both 2D and 3D data. The project required labeling camera images and LiDAR point clouds with bounding boxes, polygons, lane markings, and 3D cuboids to train self-driving vehicle perception models. Using tools like Scale AI and Supervisely, I managed a team of over 100 annotators and implemented rigorous quality control workflows to ensure high precision and consistency across millions of frames. I also conducted hands-on training and calibration sessions to align team output with client standards, ultimately contributing to the development of safer and more accurate autonomous navigation systems.

2016

Education

Work History