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D
Daoyi

Daoyi

Technology Manager in Contract Review, Compliance, and Legal Research

USA flagUsa

Key Skills

Software

No software listed

Top Subject Matter

Legal Services & Contract Review
Regulatory Compliance & Risk Analysis
Legal Research & Document Analysis

Top Data Types

Computer Code ProgrammingComputer Code Programming
TextText
DocumentDocument

Top Task Types

Computer Programming/CodingComputer Programming/Coding

Freelancer Overview

I am an AI Training Specialist with over a year of deep immersion in AI-assisted programming. I possess expert-level proficiency in advanced development environments including Cursor, Claude Code, and Codex, utilizing Vebcoding to bridge the gap between complex software logic and high-quality data annotation. My unique advantage lies in combining a developer’s technical mindset with the precision required for high-fidelity model alignment. Most recently, I spearheaded a specialized training project for the Trae IDE model. By designing rigorous labeling workflows and refining code-generation feedback loops, I consistently achieved a 90% task success rate. I excel at generating high-quality training data that enhances model reasoning and logic. My expertise ensures that LLMs move beyond simple syntax to master sophisticated, production-ready coding capabilities, making me a strategic asset for teams pushing the boundaries of generative AI.

Labeling Experience

Formal & Professional (Standard Resume Style)

Computer Code ProgrammingComputer Code ProgrammingComputer Programming/CodingComputer Programming/Coding

Project Description: Conducted deep reinforcement training for Trae IDE’s built-in programming models, optimizing code generation capabilities for complex business logic by constructing high-quality instruction datasets. Key Responsibilities: Designed and annotated high-difficulty task-code pairs utilizing toolchains such as Vebcoding and Cursor. Performed logic audits, breakpoint debugging, and performance evaluations on AI-generated code. Project Achievements: Successfully enhanced cross-file architectural understanding by implementing multi-turn feedback fine-tuning (RLHF) strategies. Achieved a consistent success rate of over 90% in real-world task testing.

2026 - 2026

Education

深圳大学

学士学位, 计算机应用与科学技术

学士学位
2020 - 2022

Work History

S

Shenzhen Yi Quan Technology

Technology Manager

Shenzhen
2025 - Present
H

Hunan Qingyun Digital Technology Service

Technology Manager & Project Manager

Changsha
2024 - 2025