Mohammed Shereif

Key Skills

Software

CVAT

Labelbox

Label Studio

LightTag

Roboflow

Supervisely

Other

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Text

Video

Top Task Types

Bounding Box

Land Cover Classification

Polygon

Text Generation

Translation Localization

Freelancer Overview

I am a skilled AI Data Labeler and Training Data Specialist with hands-on experience in Video, Audio, Image, and Text annotation for AI and machine learning projects. Proficient in bounding boxes, polygon annotation, keypoint labeling, transcription review, and text classification, I deliver accurate, high-quality datasets that enhance AI model performance. I have contributed to projects across Natural Language Processing (Arabic & English), Computer Vision, E-commerce, and Speech datasets, including text classification, sentiment analysis, content moderation, object detection, product categorization, and autonomous vehicle data. Fluent in Arabic, English, German, and Dutch, I bring added value to multilingual NLP projects, ensuring cultural and linguistic accuracy. Skilled with top tools like Label Studio, CVAT, Labelbox, LightTag, and SuperAnnotate, I excel at following complex guidelines while maintaining speed, precision, and quality. Committed to delivering reliable, consistent datasets, I support innovative AI solutions in healthcare, e-commerce, autonomous driving, and linguistic models.

Labeling Experience

Product Categorization & Tagging for E-commerce Platforms

Label StudioTextEntity Ner ClassificationClassification

Worked on a large-scale e-commerce data labeling project involving thousands of product listings. Tasks included categorizing products, tagging features, summarizing product descriptions, and identifying key entities to create structured, high-quality datasets for AI recommendation and search systems. Maintained accuracy, consistency, and adherence to classification guidelines across diverse product categories.

Worked on a large-scale e-commerce data labeling project involving thousands of product listings. Tasks included categorizing products, tagging features, summarizing product descriptions, and identifying key entities to create structured, high-quality datasets for AI recommendation and search systems. Maintained accuracy, consistency, and adherence to classification guidelines across diverse product categories.

2024

Multilingual Audio Transcription & Annotation for Speech AI

Label StudioAudioClassificationText Generation

Worked on a multilingual audio dataset for training speech recognition and virtual assistant AI models. Tasks included transcribing Arabic, English, and German audio files, labeling emotions, classifying content, and generating text from speech. Ensured precise, high-quality annotations to improve recognition accuracy and model performance. Followed strict annotation guidelines and optimized workflow for efficiency while maintaining consistency across large datasets.

Worked on a multilingual audio dataset for training speech recognition and virtual assistant AI models. Tasks included transcribing Arabic, English, and German audio files, labeling emotions, classifying content, and generating text from speech. Ensured precise, high-quality annotations to improve recognition accuracy and model performance. Followed strict annotation guidelines and optimized workflow for efficiency while maintaining consistency across large datasets.

2024

Video & Image Object Detection for Autonomous Systems

CVATVideoBounding BoxPolygon

Worked on a comprehensive video and image annotation project for autonomous driving datasets. Tasks included identifying and labeling vehicles, pedestrians, traffic signs, and road elements using bounding boxes, polygons, and keypoints. Ensured high-precision annotations to optimize object detection and tracking models. Managed large-scale datasets while following strict guidelines to maintain accuracy, consistency, and reliability across frames.

Worked on a comprehensive video and image annotation project for autonomous driving datasets. Tasks included identifying and labeling vehicles, pedestrians, traffic signs, and road elements using bounding boxes, polygons, and keypoints. Ensured high-precision annotations to optimize object detection and tracking models. Managed large-scale datasets while following strict guidelines to maintain accuracy, consistency, and reliability across frames.

2024

Multilingual Text & Audio Annotation for NLP Models

Label StudioTextEntity Ner ClassificationClassification

Worked on a comprehensive NLP training project involving text and audio datasets in multiple languages. Responsibilities included classifying texts, annotating sentiment, transcribing audio, tagging entities, and localizing content for AI models. Ensured high-quality, consistent labels that enhanced model accuracy and performance for applications such as chatbots, sentiment analysis, content moderation, and multilingual voice assistants. Successfully handled large-scale datasets while following complex guidelines, delivering reliable and precise annotations across multiple domains.

Worked on a comprehensive NLP training project involving text and audio datasets in multiple languages. Responsibilities included classifying texts, annotating sentiment, transcribing audio, tagging entities, and localizing content for AI models. Ensured high-quality, consistent labels that enhanced model accuracy and performance for applications such as chatbots, sentiment analysis, content moderation, and multilingual voice assistants. Successfully handled large-scale datasets while following complex guidelines, delivering reliable and precise annotations across multiple domains.

2024

Autonomous Vehicle Image & Video Annotation Project

CVATComputer Code ProgrammingBounding BoxPolygon

I worked on a large-scale autonomous driving project focused on training computer vision models for self-driving cars. My role involved annotating images and videos captured from multiple sensors, including LiDAR and cameras, to identify vehicles, pedestrians, traffic signs, lane markings, and environmental objects. The tasks included bounding box creation, polygonal segmentation, key point placement, and tracking moving objects across video frames to improve model accuracy for real-time object detection and navigation. The project involved labeling over 50,000 frames, adhering to strict quality guidelines with a target accuracy rate of 98%, and meeting tight deadlines. I collaborated with a quality assurance team to review and validate annotations, ensuring data integrity and readiness for model training. This project enhanced my expertise in computer vision workflows, annotation best practices, and advanced labeling techniques for the autonomous vehicle industry.

I worked on a large-scale autonomous driving project focused on training computer vision models for self-driving cars. My role involved annotating images and videos captured from multiple sensors, including LiDAR and cameras, to identify vehicles, pedestrians, traffic signs, lane markings, and environmental objects. The tasks included bounding box creation, polygonal segmentation, key point placement, and tracking moving objects across video frames to improve model accuracy for real-time object detection and navigation. The project involved labeling over 50,000 frames, adhering to strict quality guidelines with a target accuracy rate of 98%, and meeting tight deadlines. I collaborated with a quality assurance team to review and validate annotations, ensuring data integrity and readiness for model training. This project enhanced my expertise in computer vision workflows, annotation best practices, and advanced labeling techniques for the autonomous vehicle industry.

2024 - 2024

Education

Work History