Musa Styles

Key Skills

Software

AWS SageMaker

Anno-Mage

Appen

Argilla

Axiom AI

Clickworker

CloudFactory

CrowdFlower

CrowdSource

Dataloop

Figure Eight

Mercor

Mindrift

OpenCV AI Kit (OAK)

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Geospatial Tiled Imagery

Medical Dicom

Top Task Types

Audio Recording

Computer Programming Coding

Entity Ner Classification

Fine Tuning

Prompt Response Writing SFT

Freelancer Overview

With a strong background in data labeling and AI training data, I specialize in turning raw data into well-organized, high-quality annotated datasets that drive effective machine learning models. I am highly attentive to detail and skilled in using industry-standard annotation tools like Labelbox, Label Studio, and CVAT. My experience spans various projects, including computer vision, natural language processing, and reinforcement learning from human feedback, where I ensured accurate annotations for tasks such as bounding boxes, sentiment analysis, and entity recognition. This hands-on experience has strengthened my ability to contribute to building reliable AI systems through precise and consistent data labeling. In addition to my technical skills, I have a solid understanding of data privacy and industry compliance standards, which helps maintain data integrity and trustworthiness throughout the training process. My combination of technical expertise, accuracy, and commitment to quality sets me apart in the AI training data field, enabling me to support the development and deployment of innovative AI applications effectively.

Labeling Experience

Data Scientist

MercorGeospatial Tiled ImageryPolygonPoint Key Point

The project involved annotating large sets of data collected from vehicle sensors, including images, videos, and LiDAR scans. The goal was to accurately label objects such as vehicles, pedestrians, and traffic signs to train the AI models for autonomous driving. The scope was broad, covering varied environments and lighting conditions, which required detailed and consistent annotations to help the AI recognize these elements reliably in real-world situations. Quality was a top priority in the task—every annotation had to meet strict accuracy standards because even small errors could affect the safety and performance of the self-driving system. To maintain this, there were rigorous review processes and frequent validations using automated quality checks alongside human verification. The project size was substantial, involving millions of annotated frames, and required collaboration across a large team to keep pace with data volume. We strictly adhered to data privacy guidelines.

The project involved annotating large sets of data collected from vehicle sensors, including images, videos, and LiDAR scans. The goal was to accurately label objects such as vehicles, pedestrians, and traffic signs to train the AI models for autonomous driving. The scope was broad, covering varied environments and lighting conditions, which required detailed and consistent annotations to help the AI recognize these elements reliably in real-world situations. Quality was a top priority in the task—every annotation had to meet strict accuracy standards because even small errors could affect the safety and performance of the self-driving system. To maintain this, there were rigorous review processes and frequent validations using automated quality checks alongside human verification. The project size was substantial, involving millions of annotated frames, and required collaboration across a large team to keep pace with data volume. We strictly adhered to data privacy guidelines.

2024 - 2025

Machine Learning

Data Annotation Tech3D SensorBounding BoxPoint Key Point

In robotics data annotation projects, the scope generally involves creating precise, high-quality labeled datasets from diverse sources such as images, videos, LiDAR, sensor data, and 3D point clouds to train robots for complex tasks. These tasks range from object recognition and manipulation to navigation, quality inspection, and environment understanding in dynamic operational settings like warehouses, manufacturing lines, and logistics centers. The goal is to produce annotations that enable robots to interact intelligently and safely with their surroundings, adapting to various scenarios and environmental changes. The quality of annotation work is paramount, involving meticulously detailed labeling guided by strict protocols to ensure consistency and accuracy. Precision in identifying and categorizing objects, surfaces, textures, and spatial relationships directly impacts the effectiveness of the AI models that power robotic systems. Common annotation types include semantic segment

In robotics data annotation projects, the scope generally involves creating precise, high-quality labeled datasets from diverse sources such as images, videos, LiDAR, sensor data, and 3D point clouds to train robots for complex tasks. These tasks range from object recognition and manipulation to navigation, quality inspection, and environment understanding in dynamic operational settings like warehouses, manufacturing lines, and logistics centers. The goal is to produce annotations that enable robots to interact intelligently and safely with their surroundings, adapting to various scenarios and environmental changes. The quality of annotation work is paramount, involving meticulously detailed labeling guided by strict protocols to ensure consistency and accuracy. Precision in identifying and categorizing objects, surfaces, textures, and spatial relationships directly impacts the effectiveness of the AI models that power robotic systems. Common annotation types include semantic segment

2022 - 2023

Education

Work History