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Jason Yang

Jason Yang

LLM Response Quality & Reasoning Evaluator (English, Technical)

Taiwan flagTaipei, Taiwan
$25.00/hrEntry LevelAws SagemakerAxiom AI

Key Skills

Software

AWS SageMakerAWS SageMaker
Axiom AI

Top Subject Matter

Human Resources
Talent Matching
Job Market

Top Data Types

TextText
DocumentDocument

Top Task Types

ClassificationClassification
Data CollectionData Collection
Fine-tuningFine-tuning

Freelancer Overview

I have hands-on experience in AI training data and data labeling through evaluating and annotating HR-related text data for talent matching systems. My work includes classifying resumes and job descriptions, performing QA tagging, and ranking AI-generated responses based on correctness, relevance, and adherence to instructions. I have consistently applied detailed annotation guidelines to ensure high-quality, structured datasets while handling ambiguous and edge-case scenarios with consistent judgment. In addition, my background as a System Analyst in financial systems strengthens my ability to evaluate rule-based outputs and complex workflows. I am experienced in validating structured data, identifying inconsistencies, and ensuring logical correctness across systems, which directly translates to LLM response quality and reasoning evaluation. This combination of domain knowledge, analytical thinking, and guideline-driven evaluation enables me to deliver reliable and high-quality AI training data.

Entry LevelEnglish

Labeling Experience

System Analyst (AI-related labeling/evaluation tasks)

TextClassification
• Designed accounting structures and transaction mappings to support rule-based financial systems. • Analyzed end-to-end workflows from accounting to treasury disbursement, ensuring logical consistency across processes. • Validated system outputs and financial transactions for correctness, completeness, and compliance with predefined rules. • Applied rule-based reasoning to evaluate complex decision scenarios and detect inconsistencies. • Handled edge cases and ambiguous scenarios with consistent judgment aligned with policy definitions. • Translated business requirements into structured system logic and validation rules. • Experience in evaluating structured outputs and decision logic, similar to LLM response quality and reasoning evaluation.

• Designed accounting structures and transaction mappings to support rule-based financial systems. • Analyzed end-to-end workflows from accounting to treasury disbursement, ensuring logical consistency across processes. • Validated system outputs and financial transactions for correctness, completeness, and compliance with predefined rules. • Applied rule-based reasoning to evaluate complex decision scenarios and detect inconsistencies. • Handled edge cases and ambiguous scenarios with consistent judgment aligned with policy definitions. • Translated business requirements into structured system logic and validation rules. • Experience in evaluating structured outputs and decision logic, similar to LLM response quality and reasoning evaluation.

2025 - Present

LLM Data Evaluator

TextClassification
• Labeled and reviewed HR-related text data to support AI-driven talent matching and candidate recommendation systems. • Analyzed resumes and job descriptions to extract and classify structured information, including skills, job functions, seniority levels, and industry domains. • Evaluated AI-generated outputs for correctness, relevance, and alignment with real-world hiring practices in Silicon Valley tech roles. • Assessed reasoning quality in candidate-job matching results, identifying inconsistencies, misclassifications in model outputs. • Applied detailed annotation guidelines to ensure high-quality labeling across datasets, including handling ambiguous scenarios. • Performed QA tagging and validation to maintain data integrity and improve downstream model performance. • Compared multiple AI-generated responses and ranked them based on usefulness, clarity, and adherence to instructions. • Collaborated with internal teams to refine labeling guidelines, clarify edge cases, and improve annotation consistency. • Worked extensively with English-language resumes, job postings, and technical profiles in a cross-cultural, global hiring context.

• Labeled and reviewed HR-related text data to support AI-driven talent matching and candidate recommendation systems. • Analyzed resumes and job descriptions to extract and classify structured information, including skills, job functions, seniority levels, and industry domains. • Evaluated AI-generated outputs for correctness, relevance, and alignment with real-world hiring practices in Silicon Valley tech roles. • Assessed reasoning quality in candidate-job matching results, identifying inconsistencies, misclassifications in model outputs. • Applied detailed annotation guidelines to ensure high-quality labeling across datasets, including handling ambiguous scenarios. • Performed QA tagging and validation to maintain data integrity and improve downstream model performance. • Compared multiple AI-generated responses and ranked them based on usefulness, clarity, and adherence to instructions. • Collaborated with internal teams to refine labeling guidelines, clarify edge cases, and improve annotation consistency. • Worked extensively with English-language resumes, job postings, and technical profiles in a cross-cultural, global hiring context.

2024 - 2025

Education

U

University of Texas at Dallas

Master of Science, Software Engineering

Master of Science
2018 - 2020

Work History

B

Bureau of Labor Insurance

System Analyst

Taipei
2025 - Present
A

Adecco

LLM Data Evaluator

Taipei
2024 - 2025