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C

Christian Thornton

AI Data & Content Specialist (Freelance)

ExpertOtherRemotasks

Key Skills

Software

Other
RemotasksRemotasks

Top Subject Matter

Machine Learning
LLM Evaluation
Nlp Domain Expertise

Top Data Types

TextText
ImageImage
DocumentDocument

Top Task Types

RLHF
Prompt Response Writing SFT
Classification

Freelancer Overview

AI Data & Content Specialist (Freelance). Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other and Remotasks. Education includes Master of Science, Columbia University (2022) and Bachelor of Arts, Hunter College (CUNY) (2016). AI-training focus includes data types such as Text and labeling workflows including RLHF, Evaluation, and Rating.

Expert

Labeling Experience

AI Data Quality Analyst (Invincible)

OtherText
As an AI Data Quality Analyst, I designed review processes and assessed AI-generated content ahead of model training. I produced error taxonomy reports enabling targeted retraining and systematic quality improvements. My focus was on bias detection, hallucination, and instruction adherence for reliable datasets. • Designed QA review workflows for content pipelines. • Assessed outputs for bias and hallucination frequency. • Reported error taxonomy for continuous model improvement. • Helped achieve more reliable datasets for handoffs.

As an AI Data Quality Analyst, I designed review processes and assessed AI-generated content ahead of model training. I produced error taxonomy reports enabling targeted retraining and systematic quality improvements. My focus was on bias detection, hallucination, and instruction adherence for reliable datasets. • Designed QA review workflows for content pipelines. • Assessed outputs for bias and hallucination frequency. • Reported error taxonomy for continuous model improvement. • Helped achieve more reliable datasets for handoffs.

2023 - Present

LLM Training Data Specialist (Open Train)

OtherTextPrompt Response Writing SFT
In my LLM Training Data Specialist role at Open Train, I engineered prompt-response pairs for fine-tuning datasets. I validated dataset consistency and improved generalization by ensuring linguistic precision. My workflow maintained high throughput with accuracy exceeding 97%. • Created diverse LLM prompt-response pairs. • Validated datasets for consistency and factual accuracy. • Improved model generalization and reduced data noise. • Met and exceeded weekly accuracy and throughput targets.

In my LLM Training Data Specialist role at Open Train, I engineered prompt-response pairs for fine-tuning datasets. I validated dataset consistency and improved generalization by ensuring linguistic precision. My workflow maintained high throughput with accuracy exceeding 97%. • Created diverse LLM prompt-response pairs. • Validated datasets for consistency and factual accuracy. • Improved model generalization and reduced data noise. • Met and exceeded weekly accuracy and throughput targets.

2023 - Present

AI Model Evaluator & Prompt Specialist (Outlier)

OtherTextRLHF
As an AI Model Evaluator & Prompt Specialist, I assessed LLM responses and optimized prompts. I maintained quality benchmarks supporting RLHF alignment and provided structured evaluation reports. My efforts reduced model inconsistencies and accelerated retraining cycles for high-volume pipelines. • Evaluated LLM responses in reasoning, coding, and instruction-following tasks. • Optimized prompt structures and grading criteria. • Delivered reports on quality trends, error classification, and gaps. • Supported fine-tuning workflows and model alignment.

As an AI Model Evaluator & Prompt Specialist, I assessed LLM responses and optimized prompts. I maintained quality benchmarks supporting RLHF alignment and provided structured evaluation reports. My efforts reduced model inconsistencies and accelerated retraining cycles for high-volume pipelines. • Evaluated LLM responses in reasoning, coding, and instruction-following tasks. • Optimized prompt structures and grading criteria. • Delivered reports on quality trends, error classification, and gaps. • Supported fine-tuning workflows and model alignment.

2023 - Present

AI Data & Content Specialist (Freelance)

OtherTextRLHF
As an AI Data & Content Specialist, I consistently evaluated LLM outputs focusing on quality and error reduction. I validated large datasets and generated QA reports to enhance model retraining. My work enabled measurable improvements in model error rates and client deployment timelines. • Maintained a 97% acceptance rate on 150+ LLM outputs monthly. • Validated and QA'd datasets exceeding 1,000 records per cycle. • Produced weekly reports on error trends and bias patterns. • Enabled cross-team model health visibility and informed retraining decisions.

As an AI Data & Content Specialist, I consistently evaluated LLM outputs focusing on quality and error reduction. I validated large datasets and generated QA reports to enhance model retraining. My work enabled measurable improvements in model error rates and client deployment timelines. • Maintained a 97% acceptance rate on 150+ LLM outputs monthly. • Validated and QA'd datasets exceeding 1,000 records per cycle. • Produced weekly reports on error trends and bias patterns. • Enabled cross-team model health visibility and informed retraining decisions.

2023 - Present

AI Training & Data Analyst (Meta)

OtherText
While at Meta, I evaluated supervised data points for accuracy, bias, and instruction adherence. My work helped standardize annotation protocols and informed retraining priorities. The outcomes contributed to improved labeling consistency across global teams. • Evaluated 10,000+ supervised learning data points. • Achieved a 96% inter-annotator agreement score. • Identified and classified bias, hallucinations, and instruction failures. • Standardized annotation protocols for globally distributed teams.

While at Meta, I evaluated supervised data points for accuracy, bias, and instruction adherence. My work helped standardize annotation protocols and informed retraining priorities. The outcomes contributed to improved labeling consistency across global teams. • Evaluated 10,000+ supervised learning data points. • Achieved a 96% inter-annotator agreement score. • Identified and classified bias, hallucinations, and instruction failures. • Standardized annotation protocols for globally distributed teams.

2022 - 2023

Education

H

Hunter College (CUNY)

Bachelor of Arts, English

Bachelor of Arts
2012 - 2016
C

Columbia University

Master of Science, Data Science

Master of Science
2022

Work History

I

Invincible (Remote)

AI Data Quality Analyst

Location not specified
2023 - Present
O

Open Train (Remote)

LLM Training Data Specialist

Location not specified
2023 - Present