Justin H.

Key Skills

Software

Internal/Proprietary Tooling

Top Subject Matter

Legal Services & Contract Review

Regulatory Compliance & Risk Analysis

Legal Research & Document Analysis

Top Data Types

Image

Text

Document

Top Task Types

Entity (NER) Classification

Bounding Box

Classification

Freelancer Overview

Professional Summary Deeply entrenched in the field of data annotation and AI training data, I specialize in the Reinforcement Learning from Human Feedback (RLHF) and Supervised Fine-Tuning (SFT) phases of Large Language Models (LLMs), with a dedicated focus on high-difficulty logical reasoning, code development, multi-turn dialogues, and Agent decision chains. I possess exceptional capabilities in vulnerability auditing and rigorous logic validation, and I excel at benchmarking and conducting adversarial red-teaming tests on AI-generated code. When processing highly complex technical data, I can precisely identify model hallucinations and establish stringent scoring criteria for reward models. I have driven targeted training initiatives across multiple cutting-edge AI domains. In automation and matrix engineering environments, I have translated hard-core business logic—such as multi-device synchronized control and weak-network fault tolerance—into high-quality prompt datasets. Furthermore, in cross-disciplinary projects involving Web3 and decentralized AI, I have successfully executed hundreds of high-precision Chain-of-Thought (CoT) annotations for decentralized node synchronization logic and on-chain whale wallet transaction data extraction. This "full-stack" perspective, which spans from hardware-software infrastructure to data feedback loops, allows me to generate AI training corpora with robust logic and deep technical moats, giving me a distinct advantage in accelerating the iteration of models for complex reasoning.

Labeling Experience

Robotic Trajectory and Action Annotation

ImageObject Detection

Robotic Trajectory and Action Annotation

2025 - 2026

Education

Work History