Daniel T.

Key Skills

Software

Data Annotation Tech

Top Subject Matter

AI Code Generation

Legal Services & Contract Review

Regulatory Compliance & Risk Analysis

Top Data Types

Computer Code Programming

Text

Document

Top Task Types

RLHF

Freelancer Overview

My foundational experience in AI data formatting and evaluation stems from my role as an AI Trainer at Data Annotation Tech, where I specialized in assessing the accuracy, efficiency, and logical soundness of code generated by large language models. In this capacity, I generated high-quality, meticulously annotated datasets designed to train and refine AI models utilizing Reinforcement Learning from Human Feedback (RLHF) methodologies. Operating across multiple concurrent machine learning evaluation projects, I developed a strong track record of managing independent, remote workflows while maintaining strict adherence to complex, highly specific prompt guidelines and quality assurance standards. What sets me apart in the AI training space is my rigorous technical background and hands-on experience architecting complex, AI-integrated software systems. As I progress toward a Master of Science in AI Engineering, I bring deep proficiency in languages like Python, Rust, and TypeScript to my code evaluations. Rather than simply checking for basic syntax errors, I can critically analyze algorithmic efficiency, memory management, and system architecture. I actively apply these skills in my own work, such as developing RavenChess—a high-performance, 64-bit chess engine written in Rust and bridged to Python via PyO3, which I am actively engineering for machine learning inference via ONNX. This combination of formal software engineering training, practical full-stack development, and dedicated RLHF experience enables me to expertly navigate technical edge-case reviews and complex code evaluations.

Labeling Experience

AI Trainer (Contract)

Data Annotation TechRLHF

As an AI Trainer at Data Annotation Tech, I evaluated the performance of code produced by large language models and contributed directly to the RLHF training pipeline. I created annotated datasets by reviewing outputs and applying prompt guidelines to ensure AI accuracy and reliability. My responsibilities included both qualitative and quantitative assessments to refine and optimize AI behavior. • Evaluated code outputs for accuracy, efficiency, and logical soundness. • Created high-quality datasets for RLHF-based AI training. • Operated independently on multiple ML evaluation projects. • Ensured adherence to strict prompt-specific guidelines.

2024 - 2024

Education

Work History