Jamia Daniels

Key Skills

Software

Appen

Axiom AI

Clickworker

CloudFactory

CrowdFlower

CrowdSource

Data Annotation Tech

Dataturk

HiveMind

Labelbox

LabelImg

Label Studio

Mighty AI

Mindrift

OneForma

Prodigy

Redbrick AI

Remotasks

Scale AI

Snorkel AI

SuperAnnotate

Surge AI

Toloka

Telus

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Text

Top Task Types

Classification

Computer Programming Coding

Evaluation Rating

Prompt Response Writing SFT

RLHF

Freelancer Overview

As an experienced AI training specialist, I possess a strong background in enhancing the performance of artificial intelligence platforms, with a particular focus on the complex domains of mathematics and computer science. My expertise lies in the full spectrum of data preparation, including meticulous data labeling, annotation, and the development of clear, concise training guidelines. I have a proven ability to ensure the quality and accuracy of training data, which is essential for building robust and reliable AI models. This involves a deep understanding of the subject matter to provide nuanced and contextually appropriate labels, ensuring the models can grasp intricate concepts. My hands-on experience extends to coding and scripting to automate and streamline the data preparation process. I have collaborated with cross-functional teams of data scientists and engineers to refine machine learning models by providing high-quality, annotated datasets. This has involved developing and implementing novel data augmentation techniques to improve model generalization across diverse datasets. My analytical and problem-solving skills, honed through my background in mathematics, enable me to identify and rectify inconsistencies or biases within datasets, ultimately contributing to the development of more accurate and effective AI systems.

Labeling Experience

Mathematical Problem and Solution Annotation for AI Tutor

AppenTextClassificationQuestion Answering

This project focused on building a robust dataset to train an AI-powered tutoring system for high school and university-level mathematics. My role involved meticulously annotating a diverse range of mathematical problems, from algebra and geometry to advanced calculus and linear algebra. I was responsible for transcribing handwritten problems from scanned images, classifying each problem by its mathematical domain, and generating detailed, step-by-step solutions. A critical part of the project was not just providing the correct answer, but also authoring clear, pedagogical explanations for each step in a "Text Generation" task, effectively teaching the problem-solving process. Using Named Entity Recognition (NER), I tagged key mathematical concepts, theorems, and variables within the problems and solutions.

This project focused on building a robust dataset to train an AI-powered tutoring system for high school and university-level mathematics. My role involved meticulously annotating a diverse range of mathematical problems, from algebra and geometry to advanced calculus and linear algebra. I was responsible for transcribing handwritten problems from scanned images, classifying each problem by its mathematical domain, and generating detailed, step-by-step solutions. A critical part of the project was not just providing the correct answer, but also authoring clear, pedagogical explanations for each step in a "Text Generation" task, effectively teaching the problem-solving process. Using Named Entity Recognition (NER), I tagged key mathematical concepts, theorems, and variables within the problems and solutions.

2024 - 2025

Enhancing LLM Code Generation through Expert-Led Fine-Tuning and RLHF

MindriftComputer Code ProgrammingQuestion AnsweringText Summarization

This project focused on improving the code generation capabilities of a proprietary large language model, with a special emphasis on mathematical and algorithmic problem-solving. My role involved a multi-faceted approach to create high-quality training data. I was responsible for writing and curating a large dataset of instructional prompts and ideal code responses for Supervised Fine-Tuning (SFT). This required a deep understanding of various programming languages and computer science concepts to generate accurate, efficient, and well-documented code samples. Following the initial fine-tuning, I played a critical role in the Reinforcement Learning from Human Feedback (RLHF) phase. This involved meticulously evaluating and ranking multiple AI-generated code outputs based on a comprehensive set of criteria, including correctness, efficiency, readability, and adherence to best practices. I also performed detailed code annotation, classifying bug types, identifying logical errors, and us

This project focused on improving the code generation capabilities of a proprietary large language model, with a special emphasis on mathematical and algorithmic problem-solving. My role involved a multi-faceted approach to create high-quality training data. I was responsible for writing and curating a large dataset of instructional prompts and ideal code responses for Supervised Fine-Tuning (SFT). This required a deep understanding of various programming languages and computer science concepts to generate accurate, efficient, and well-documented code samples. Following the initial fine-tuning, I played a critical role in the Reinforcement Learning from Human Feedback (RLHF) phase. This involved meticulously evaluating and ranking multiple AI-generated code outputs based on a comprehensive set of criteria, including correctness, efficiency, readability, and adherence to best practices. I also performed detailed code annotation, classifying bug types, identifying logical errors, and us

2022 - 2023

Education

Work History