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Taeseok Lee

Taeseok Lee

Mechanical Engineering Domain Expert for LLM Evaluation & RLHF

South Korea flagAnsan, South Korea
$50.00/hrIntermediateOther

Key Skills

Software

Other

Top Subject Matter

No subject matter listed

Top Data Types

DocumentDocument
ImageImage
TextText

Top Task Types

Bounding Box
Data Collection
Text Summarization
Translation Localization

Freelancer Overview

I am a Mechanical Engineering domain expert with a specialized background in structural analysis and CAE simulation (Hypermesh/OptiStruct). My core strength lies in the ability to critically evaluate and correct AI-generated technical content. I have practical experience leveraging Large Language Models (LLMs) to troubleshoot engineering code, where I successfully identified and fixed "hallucinations" (logical errors) in boundary condition settings that violated physical laws. This experience demonstrates my capability to provide high-quality Ground Truth data for technical AI training and RLHF (Reinforcement Learning from Human Feedback) tasks, ensuring model accuracy in specialized engineering fields. In addition to technical expertise, I bring strong analytical and communication skills honed through social impact projects like the Kakao Impact Challenge. I am adept at understanding complex contexts, verifying logical consistency, and managing diverse data types with precision. Whether the task involves complex engineering problems, code evaluation, or general data annotation requiring high attention to detail, I am committed to delivering reliable, verified training data that enhances the safety and performance of AI systems.

IntermediateEnglishJapaneseChinese Mandarin

Labeling Experience

Engineering Code Evaluation & Debugging for Structural Analysis

OtherTextRLHFEvaluation Rating
Evaluated and corrected AI-generated code snippets (Tcl/Tk script) for Hypermesh (OptiStruct) topology optimization. The Generative AI model initially produced 'hallucinations' regarding boundary conditions (SPC) and mesh connectivity, which violated physical laws. My role involved: 1. Code Review: Analyzed the AI-generated FEA solver deck to identify logical errors. 2. Debugging & Editing: Applied mechanical engineering domain knowledge to correct the parameters and syntax, establishing the 'Ground Truth' code. 3. Validation: Verified the final script by running simulations to ensure successful convergence and structural integrity. This experience demonstrates strong capabilities in RLHF (Reinforcement Learning from Human Feedback) for technical and engineering domains.

Evaluated and corrected AI-generated code snippets (Tcl/Tk script) for Hypermesh (OptiStruct) topology optimization. The Generative AI model initially produced 'hallucinations' regarding boundary conditions (SPC) and mesh connectivity, which violated physical laws. My role involved: 1. Code Review: Analyzed the AI-generated FEA solver deck to identify logical errors. 2. Debugging & Editing: Applied mechanical engineering domain knowledge to correct the parameters and syntax, establishing the 'Ground Truth' code. 3. Validation: Verified the final script by running simulations to ensure successful convergence and structural integrity. This experience demonstrates strong capabilities in RLHF (Reinforcement Learning from Human Feedback) for technical and engineering domains.

2025 - 2025

Education

H

Hanyang University ERICA

Bachelor of Science, Mechanical Engineering

Bachelor of Science
2020 - 2025

Work History

H

Hanyang University ERICA

Technical Data Analyst & Annotator

Ansan
2020 - 2025
D

Datamaker.io

Technical Data Analyst & Annotator

Ansan
2020 - 2025