Jesse Reitzel

Key Skills

Software

Appen

Lionbridge

Telus

Top Subject Matter

Large Language Model Evaluation and Training

NLP Data Annotation and Model Evaluation

AI Content Moderation and Evaluation

Top Data Types

Text

Document

Audio

Top Task Types

Entity (NER) Classification

Text Generation

Text Summarization

Object Detection

Transcription

Data Collection

Evaluation/Rating

Freelancer Overview

AI Training & Evaluation Specialist. Core strengths include Appen, Lionbridge, and Telus. Education includes Bachelor of Science, Texas Tech University (2020). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Entity (NER) Classification.

Labeling Experience

AI Training & Evaluation Specialist

AppenText

As an AI Training & Evaluation Specialist at Appen, I focused on evaluating and improving large language models through structured review and adversarial testing. My work involved prompt engineering, data annotation, and designing gold-standard benchmarks to enhance model performance. I developed thousands of high-quality training samples and provided data-driven feedback for model fine-tuning. • Evaluated LLM outputs for reasoning accuracy, instruction adherence, and hallucinations. • Designed adversarial prompts and contributed to red-teaming efforts. • Created benchmark datasets and gold-standard responses. • Identified and documented recurring quality issues for model improvement.

As an AI Training & Evaluation Specialist at Appen, I focused on evaluating and improving large language models through structured review and adversarial testing. My work involved prompt engineering, data annotation, and designing gold-standard benchmarks to enhance model performance. I developed thousands of high-quality training samples and provided data-driven feedback for model fine-tuning. • Evaluated LLM outputs for reasoning accuracy, instruction adherence, and hallucinations. • Designed adversarial prompts and contributed to red-teaming efforts. • Created benchmark datasets and gold-standard responses. • Identified and documented recurring quality issues for model improvement.

2022 - Present

AI Data Analyst / NLP Model Evaluator

LionbridgeTextEntity Ner Classification

At Lionbridge, I worked as an AI Data Analyst and NLP Model Evaluator, focusing on annotating large-scale NLP datasets for AI development. My tasks involved labeling, quality review, and support for multilingual evaluation projects. I applied structured validation and comparison techniques to optimize dataset quality and safety. • Labeled and annotated transformer model datasets for NLP tasks. • Conducted quality reviews and ensured annotation consistency. • Participated in side-by-side model evaluations (A/B testing). • Supported dataset preparation for AI training and validation.

At Lionbridge, I worked as an AI Data Analyst and NLP Model Evaluator, focusing on annotating large-scale NLP datasets for AI development. My tasks involved labeling, quality review, and support for multilingual evaluation projects. I applied structured validation and comparison techniques to optimize dataset quality and safety. • Labeled and annotated transformer model datasets for NLP tasks. • Conducted quality reviews and ensured annotation consistency. • Participated in side-by-side model evaluations (A/B testing). • Supported dataset preparation for AI training and validation.

2021 - 2022

AI Content Quality Reviewer

TelusText

As an AI Content Quality Reviewer at TELUS AI, I reviewed model outputs for safety, policy compliance, and bias. This role required detailed documentation of rejection decisions and strong analytical feedback. My work contributed to training data quality for conversational AI systems and maintaining compliance standards. • Reviewed AI-generated content for quality and alignment. • Used internal guidelines for bias and safety evaluation. • Flagged and documented high-risk outputs. • Provided rationale for data labeling and quality decisions.

As an AI Content Quality Reviewer at TELUS AI, I reviewed model outputs for safety, policy compliance, and bias. This role required detailed documentation of rejection decisions and strong analytical feedback. My work contributed to training data quality for conversational AI systems and maintaining compliance standards. • Reviewed AI-generated content for quality and alignment. • Used internal guidelines for bias and safety evaluation. • Flagged and documented high-risk outputs. • Provided rationale for data labeling and quality decisions.

2020 - 2021

Education

Work History