Mohammed Rizgallah

Key Skills

Software

Sama

CVAT

CloudFactory

Encord

Top Subject Matter

Computer Vision (Visual Recognition)

Autonomous Driving AI

Machine Learning (Large-Scale Datasets)

Top Data Types

Image

3D Sensor

Video

Top Task Types

Segmentation

Bounding Box

Polygon

Object Detection

Classification

Prompt + Response Writing (SFT)

Freelancer Overview

Image Labeling & Segmentation Specialist. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Sama. Education includes Certificate in Journalism, East Africa Institute of Certified Studies (2022) and Kenya Certificate of Secondary Education, Olympic High School (2017). AI-training focus includes data types such as Image and 3D Sensor and labeling workflows including Segmentation.

Labeling Experience

Advanced Image Annotation Lead

SamaImageSegmentation

From 2023 to 2025, I managed advanced image annotation processes for large-scale AI datasets. My role involved leading high-volume, precision labeling tasks and consistently exceeding throughput and accuracy benchmarks. I contributed directly to multiple AI model deployments through meticulous labeling of complex objects. • Supervised and executed precision labeling projects for extensive datasets. • Developed and optimized annotation protocols. • Improved dataset quality and reduced labeling errors by 20%. • Facilitated collaboration between annotation, engineering, and research teams.

From 2023 to 2025, I managed advanced image annotation processes for large-scale AI datasets. My role involved leading high-volume, precision labeling tasks and consistently exceeding throughput and accuracy benchmarks. I contributed directly to multiple AI model deployments through meticulous labeling of complex objects. • Supervised and executed precision labeling projects for extensive datasets. • Developed and optimized annotation protocols. • Improved dataset quality and reduced labeling errors by 20%. • Facilitated collaboration between annotation, engineering, and research teams.

2023 - 2025

LiDAR Video Annotation Specialist

Sama3D SensorSegmentation

In 2021 and 2022, I performed LiDAR video annotation for autonomous driving AI projects. My responsibilities included annotating complex 3D spatial data to enable improved object detection algorithms. This involved detailed segmentation of objects and environments in LiDAR video data. • Labeled thousands of frames of spatial 3D LiDAR data. • Ensured high accuracy in object boundary definition for self-driving AI. • Worked directly with data scientists and AI engineers. • Supported validation and quality assurance processes for annotated datasets.

In 2021 and 2022, I performed LiDAR video annotation for autonomous driving AI projects. My responsibilities included annotating complex 3D spatial data to enable improved object detection algorithms. This involved detailed segmentation of objects and environments in LiDAR video data. • Labeled thousands of frames of spatial 3D LiDAR data. • Ensured high accuracy in object boundary definition for self-driving AI. • Worked directly with data scientists and AI engineers. • Supported validation and quality assurance processes for annotated datasets.

2021 - 2022

Image Labeling & Segmentation Specialist

SamaImageSegmentation

From 2018 to 2020, I worked on enhancing visual recognition datasets through precise image labeling and segmentation. My work improved AI model accuracy by up to 15% and adhered strictly to annotation standards. The labeling involved defining object boundaries and ensuring high data quality for machine learning projects. • Maintained quality and accuracy targets in visual dataset creation. • Coordinated with project managers for high-priority AI tasks. • Followed strict annotation protocols and guidelines. • Supported AI model improvements through consistent data validation.

From 2018 to 2020, I worked on enhancing visual recognition datasets through precise image labeling and segmentation. My work improved AI model accuracy by up to 15% and adhered strictly to annotation standards. The labeling involved defining object boundaries and ensuring high data quality for machine learning projects. • Maintained quality and accuracy targets in visual dataset creation. • Coordinated with project managers for high-priority AI tasks. • Followed strict annotation protocols and guidelines. • Supported AI model improvements through consistent data validation.

2018 - 2020

Education

Work History