Rashid Said

Key Skills

Software

Appen

Clickworker

CloudFactory

CVAT

Data Annotation Tech

Hasty

Mindrift

OneForma

Toloka

Telus

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Audio

Image

Top Task Types

Bounding Box

Computer Programming/Coding

Cuboid

Polygon

Segmentation

Freelancer Overview

I am an experienced data annotation specialist with a strong background in computer science and over 4+ years of hands-on work in AI training data projects. I’ve contributed to high-quality image, video, and text labeling tasks, supporting the development of machine learning models in fields such as computer vision and natural language processing. My expertise includes 2D image annotation, bounding boxes, segmentation, relationship labeling, text classification, and LLM evaluation. I have used industry-standard tools like CVAT, Labelbox, Hasty AI, and various in-house platforms to complete projects with speed and accuracy. With a sharp eye for detail and the ability to follow complex guidelines, I consistently deliver precise annotations that align with project goals. My diverse background, including experience in architecture and software development, enables me to bring both analytical and visual strengths to every task.

Labeling Experience

Text Classification and Relationship Annotation for NLP Models

AppenTextEntity Ner ClassificationClassification

This project involved annotating entities and classifying relationships between them to support training data for large language models (LLMs). I reviewed and labelled thousands of text samples, focusing on identifying named entities (people, organisations, locations) and accurately classifying their relationships within context. I also evaluated model outputs for relevance, coherence, and correctness. The work required deep contextual understanding and strict alignment with evolving guidelines. My annotations directly supported improvements in LLM accuracy and contextual reasoning.

This project involved annotating entities and classifying relationships between them to support training data for large language models (LLMs). I reviewed and labelled thousands of text samples, focusing on identifying named entities (people, organisations, locations) and accurately classifying their relationships within context. I also evaluated model outputs for relevance, coherence, and correctness. The work required deep contextual understanding and strict alignment with evolving guidelines. My annotations directly supported improvements in LLM accuracy and contextual reasoning.

2023 - 2023

Autonomous Vehicle Image and Video Annotation

CVAT3D SensorBounding BoxPolygon

I worked on a large-scale computer vision project focused on training perception models for autonomous vehicles. My tasks included drawing accurate bounding boxes and polygons around various road elements such as cars, pedestrians, traffic signs, and lane markings. I also performed instance segmentation and object tracking across video frames to ensure temporal consistency. The project required strict adherence to annotation guidelines, high precision, and regular QA checks. I consistently met quality benchmarks above 95% accuracy and contributed to improving model performance through consistent, detailed annotations.

I worked on a large-scale computer vision project focused on training perception models for autonomous vehicles. My tasks included drawing accurate bounding boxes and polygons around various road elements such as cars, pedestrians, traffic signs, and lane markings. I also performed instance segmentation and object tracking across video frames to ensure temporal consistency. The project required strict adherence to annotation guidelines, high precision, and regular QA checks. I consistently met quality benchmarks above 95% accuracy and contributed to improving model performance through consistent, detailed annotations.

2021 - 2022

Education

Work History