Amanda Carey

Key Skills

Software

Clickworker

Top Subject Matter

Large Language Models/General AI

AI Content Moderation & Logic Assessment

Machine Learning Data Quality

Top Data Types

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Image

Top Task Types

RLHF

Freelancer Overview

AI Generalist (Freelance). Core strengths include Internal, Proprietary Tooling, and Clickworker. Education includes Bachelor of Science, Kansas State University (2023). AI-training focus includes data types such as Text and labeling workflows including RLHF, Evaluation, and Rating.

Labeling Experience

AI Generalist (Freelance)

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As an AI Training Specialist, I led prompt engineering and reinforcement learning from human feedback for major LLM projects. My efforts included evaluating thousands of AI-generated responses for accuracy and providing detailed analysis on instruction adherence and logical coherence. I played a key role in identifying and reporting model hallucinations to advance model reliability. • Evaluated 2,500+ text outputs for factual accuracy and coherence • Delivered in-depth feedback for model improvement cycles • Ensured model outputs met rigorous quality standards and project criteria • Worked directly with OpenAI-related teams on prompt engineering tasks.

As an AI Training Specialist, I led prompt engineering and reinforcement learning from human feedback for major LLM projects. My efforts included evaluating thousands of AI-generated responses for accuracy and providing detailed analysis on instruction adherence and logical coherence. I played a key role in identifying and reporting model hallucinations to advance model reliability. • Evaluated 2,500+ text outputs for factual accuracy and coherence • Delivered in-depth feedback for model improvement cycles • Ensured model outputs met rigorous quality standards and project criteria • Worked directly with OpenAI-related teams on prompt engineering tasks.

2025 - Present

AI Data Evaluator

ClickworkerText

In this role, I performed large-scale data labeling focusing on reasoning and logic verification for AI training datasets. My responsibilities included maintaining high quality scores across thousands of assignments and ensuring compliance with ethical guidelines. I contributed to enhanced team throughput through clear interpretation of data guidelines. • Labeled over 5,000 text samples for logic verification • Detected and flagged harmful or biased AI outputs • Consistently recognized as a Top Tier Contributor platform-wide • Enhanced workflow efficiency by streamlining guideline clarity for teammates.

In this role, I performed large-scale data labeling focusing on reasoning and logic verification for AI training datasets. My responsibilities included maintaining high quality scores across thousands of assignments and ensuring compliance with ethical guidelines. I contributed to enhanced team throughput through clear interpretation of data guidelines. • Labeled over 5,000 text samples for logic verification • Detected and flagged harmful or biased AI outputs • Consistently recognized as a Top Tier Contributor platform-wide • Enhanced workflow efficiency by streamlining guideline clarity for teammates.

2024 - 2024

Junior AI Specialist

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As a Junior AI Specialist, I managed and cleaned datasets for machine learning and reviewed labeled data for accuracy. I systematically reduced labeling errors by conducting detailed quality checks and collaborated to report repeated model failures. My actions supported overall data integrity and improvement of training pipelines. • Improved dataset usability for AI model development • Reduced labeling errors by 25% via protocol-driven reviews • Reported and documented recurring annotation issues • Interfaced with cross-functional engineering and training teams.

As a Junior AI Specialist, I managed and cleaned datasets for machine learning and reviewed labeled data for accuracy. I systematically reduced labeling errors by conducting detailed quality checks and collaborated to report repeated model failures. My actions supported overall data integrity and improvement of training pipelines. • Improved dataset usability for AI model development • Reduced labeling errors by 25% via protocol-driven reviews • Reported and documented recurring annotation issues • Interfaced with cross-functional engineering and training teams.

2023 - 2024

Education

Work History