Mahmoud Salah

Key Skills

Software

AWS SageMaker

CVAT

Dataturk

Labelbox

Roboflow

Scale AI

Supervisely

Top Subject Matter

Autonomous Vehicles – Labeling data related to self-driving cars, such as traffic signs, road markings, and object detection for safe navigation.

Sports – Labeling sports-related data for player tracking, action recognition, and performance analysis, typically used for AI models in sports analytics.

Healthcare – Labeling medical images (e.g., X-rays, MRIs), patient data, or other healthcare-related data for applications in diagnostics, predictive modeling, or medical research.

Top Data Types

Image

Text

Video

Top Task Types

Action Recognition

Bounding Box

Classification

Point Key Point

Segmentation

Freelancer Overview

As a Data Labeling Specialist with over 2 years of experience, I have developed expertise in annotating and preparing high-quality datasets for AI and machine learning models across various domains, including healthcare, automotive, and e-commerce. I specialize in image segmentation, object detection, text classification, and entity recognition, and I am proficient in using leading tools like Labelbox, Supervisely, and CVAT to ensure the accuracy and efficiency of data labeling processes. Throughout my career, I have worked on multiple high-impact projects, including labeling medical images for diagnostic models, annotating product data for e-commerce platforms, and supporting AI-driven systems for autonomous vehicles. My work has led to measurable improvements in model performance, contributing to enhanced accuracy and reduced error rates. With a strong commitment to delivering clean, reliable data, I am dedicated to supporting AI and machine learning models to achieve optimal results.

Labeling Experience

Video Annotation for Autonomous Vehicle Training

SuperviselyVideoSegmentationObject Detection

Annotated video data from dash cameras in autonomous vehicles to detect and track objects such as pedestrians, other vehicles, traffic signals, and road signs. The task involved labeling each frame with bounding boxes around these objects and assigning appropriate labels for training AI models in object detection and motion prediction for self-driving cars.

Annotated video data from dash cameras in autonomous vehicles to detect and track objects such as pedestrians, other vehicles, traffic signals, and road signs. The task involved labeling each frame with bounding boxes around these objects and assigning appropriate labels for training AI models in object detection and motion prediction for self-driving cars.

2024 - 2024

Data Labeling for Python Code Generation and Optimization

Scale AIComputer Code ProgrammingRLHFComputer Programming Coding

In this project, I worked on enhancing auto-generated Python code using Reinforcement Learning with Human Feedback (RLHF). My role included reviewing, correcting, and annotating Python scripts produced by AI models. I provided constructive feedback on various aspects of the code such as logic, performance, and adherence to best practices. Tasks included labeling function calls, reviewing code outputs for efficiency, and offering optimization suggestions. I also provided feedback to improve model performance, ensuring the generated code was both accurate and efficient. The project size consisted of thousands of Python code snippets, focused on algorithms, data structures, and various Python libraries.

In this project, I worked on enhancing auto-generated Python code using Reinforcement Learning with Human Feedback (RLHF). My role included reviewing, correcting, and annotating Python scripts produced by AI models. I provided constructive feedback on various aspects of the code such as logic, performance, and adherence to best practices. Tasks included labeling function calls, reviewing code outputs for efficiency, and offering optimization suggestions. I also provided feedback to improve model performance, ensuring the generated code was both accurate and efficient. The project size consisted of thousands of Python code snippets, focused on algorithms, data structures, and various Python libraries.

2023 - 2023

Text Categorization for Customer Feedback Analysis

Aws SagemakerTextEntity Ner ClassificationClassification

Labeled customer feedback data to classify reviews into categories such as “Product Quality”, “Shipping Issues”, and “Customer Service” for deeper analysis. Additionally, extracted key entities like product names and ratings using Named Entity Recognition to help enhance automatic feedback categorization systems.

Labeled customer feedback data to classify reviews into categories such as “Product Quality”, “Shipping Issues”, and “Customer Service” for deeper analysis. Additionally, extracted key entities like product names and ratings using Named Entity Recognition to help enhance automatic feedback categorization systems.

2023 - 2023

Image Annotation for Object Detection in Autonomous Vehicles

CVATImageSegmentationObject Detection

Annotated images for training object detection models, focusing on vehicle identification, pedestrian detection, and traffic sign recognition for autonomous vehicle systems. This project involved high-precision annotations to support safe driving algorithms.

Annotated images for training object detection models, focusing on vehicle identification, pedestrian detection, and traffic sign recognition for autonomous vehicle systems. This project involved high-precision annotations to support safe driving algorithms.

2023 - 2023

Medical Text Classification for Electronic Health Records (EHR)

LabelboxTextClassificationText Summarization

Labeled patient medical records for classifying different sections of Electronic Health Records (EHR) such as “Diagnosis”, “Treatment Plan”, and “Patient History”. Also, used entity annotation to extract critical medical terms such as “medication names”, “dosage”, and “symptoms”. This project aimed to automate the extraction of important clinical data to support predictive healthcare analytics.

Labeled patient medical records for classifying different sections of Electronic Health Records (EHR) such as “Diagnosis”, “Treatment Plan”, and “Patient History”. Also, used entity annotation to extract critical medical terms such as “medication names”, “dosage”, and “symptoms”. This project aimed to automate the extraction of important clinical data to support predictive healthcare analytics.

2022 - 2022

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Education

Work History