Aamna Ahmed

Key Skills

Software

CrowdSource

Data Annotation Tech

OneForma

Remotasks

Telus

Other

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Task Types

Classification

Data Collection

Evaluation/Rating

Fine-tuning

Question Answering

Freelancer Overview

I have over two years of hands-on experience in data labeling and AI training data across a wide range of industries, including large language model (LLM) evaluation, and general computer vision projects. I have worked with top AI companies such as Stellar AI, Outlier AI, and others, contributing to high-accuracy datasets for machine learning model development. My expertise includes image and video annotation, text classification, data evaluation, and quality assurance of labeled data. I am skilled in following detailed project guidelines, maintaining high labeling accuracy, and adapting quickly to new domains and annotation tools. My strong background in both technical labeling and model evaluation enables me to deliver reliable, scalable training data for cutting-edge AI systems.

Labeling Experience

Web Browser

OtherTextObject DetectionAction Recognition

The Web Browser Project at Stellar AI involved evaluating and improving AI agents that interact with web browsers to complete tasks. Taskers tested AI models by providing prompts and assessing how well the AI could navigate websites, search for information, fill out forms, click buttons, and accomplish specific goals. The project required a strong understanding of web interfaces, careful attention to following task guidelines, and the ability to provide detailed feedback on model performance. This work helped train AI systems to perform real-world web browsing tasks more accurately, efficiently, and safely.

The Web Browser Project at Stellar AI involved evaluating and improving AI agents that interact with web browsers to complete tasks. Taskers tested AI models by providing prompts and assessing how well the AI could navigate websites, search for information, fill out forms, click buttons, and accomplish specific goals. The project required a strong understanding of web interfaces, careful attention to following task guidelines, and the ability to provide detailed feedback on model performance. This work helped train AI systems to perform real-world web browsing tasks more accurately, efficiently, and safely.

2024

Dolphin

Scale AITextClassificationQuestion Answering

The Dolphin Project on Remotasks focused on text analysis tasks, primarily involving sentiment analysis and text classification. Taskers were responsible for reading short pieces of text, such as social media posts, reviews, or conversations, and labeling them according to their emotional tone (positive, negative, neutral) or categorizing them into specific topics or intents. The goal was to create high-quality labeled datasets to help train and improve AI models in understanding human language and emotions more accurately. Attention to detail, consistency with guidelines, and an understanding of nuanced language were critical skills for success in the Dolphin project.

The Dolphin Project on Remotasks focused on text analysis tasks, primarily involving sentiment analysis and text classification. Taskers were responsible for reading short pieces of text, such as social media posts, reviews, or conversations, and labeling them according to their emotional tone (positive, negative, neutral) or categorizing them into specific topics or intents. The goal was to create high-quality labeled datasets to help train and improve AI models in understanding human language and emotions more accurately. Attention to detail, consistency with guidelines, and an understanding of nuanced language were critical skills for success in the Dolphin project.

2023 - 2023

Flamingo

Scale AITextClassificationQuestion Answering

The Flamingo Project on Remotasks involved evaluating and refining AI-generated text to improve the performance of large language models (LLMs). As a tasker, I reviewed AI responses for relevance, accuracy, coherence, and overall quality, using detailed rubrics to provide structured feedback. The work included ranking model outputs, correcting errors, selecting the better of two responses, and occasionally generating ideal examples. This project contributed to the fine-tuning and alignment of conversational AI systems, ensuring they were more helpful, safe, and user-aligned.

The Flamingo Project on Remotasks involved evaluating and refining AI-generated text to improve the performance of large language models (LLMs). As a tasker, I reviewed AI responses for relevance, accuracy, coherence, and overall quality, using detailed rubrics to provide structured feedback. The work included ranking model outputs, correcting errors, selecting the better of two responses, and occasionally generating ideal examples. This project contributed to the fine-tuning and alignment of conversational AI systems, ensuring they were more helpful, safe, and user-aligned.

2022 - 2023

Education

Work History