Chrissean Body

Key Skills

Software

AWS SageMaker

Anno-Mage

Appen

Axiom AI

Clickworker

CloudFactory

Datature

Deep Systems

Diffgram

Labelbox

Mindrift

OneForma

Remotasks

Sama

Snorkel AI

Trilldata Technologies

V7 Labs

Scale AI

CVAT

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Video

Top Task Types

Computer Programming Coding

Prompt Response Writing SFT

RLHF

Text Generation

Text Summarization

Freelancer Overview

I am an experienced AI Evaluation and Data Labeling Specialist with over five years of hands-on work in rubric-based review, LLM evaluation, and multi-modal annotation. My expertise covers prompt and response analysis, RLHF, SFT, classification, and adversarial red-teaming. I specialize in detecting factual inaccuracies, bias, ambiguity, and policy non-compliance while delivering consistent, high-quality outputs that align with evolving guidelines. I have worked across multiple vendor platforms, including Scale AI, Surge AI, DataAnnotation.Tech, Outlier, Appen, TELUS International, and TaskUs, consistently maintaining accuracy rates above 98%. With a Master’s in Language Technology and a Bachelor’s in English and Linguistics from the University of Alabama, I bring both academic depth and applied experience in annotation frameworks, computational linguistics, and evaluation design. My background in teaching, copyediting, and auditing strengthens my ability to apply detailed rubrics, provide actionable feedback, and ensure cross-team consistency. I excel in high-volume, deadline-driven environments and am passionate about improving model fairness, safety, and overall performance through careful, detail-oriented evaluation.

Labeling Experience

Academic Linguistics & Annotation Research

Snorkel AITextEntity Ner ClassificationRelationship

As part of graduate-level research in Language Technology, designed and applied annotation frameworks for syntax, semantics, and classification tasks. Built experimental rubrics to measure inter-rater consistency and applied hybrid rule-based/ML models to analyze annotated corpora. Delivered reports on annotation quality and evaluator agreement that informed faculty research and course development. This work provided the academic foundation for professional success in AI data labeling and evaluation.

As part of graduate-level research in Language Technology, designed and applied annotation frameworks for syntax, semantics, and classification tasks. Built experimental rubrics to measure inter-rater consistency and applied hybrid rule-based/ML models to analyze annotated corpora. Delivered reports on annotation quality and evaluator agreement that informed faculty research and course development. This work provided the academic foundation for professional success in AI data labeling and evaluation.

2023 - 2025

LLM Prompt and Response Evaluation

Scale AITextClassificationText Generation

Evaluated thousands of AI-generated responses for accuracy, clarity, fairness, and adherence to policy guidelines. Performed ranking tasks (RLHF) to improve reward model training, applied detailed rubrics to assess ambiguity, bias, and factual correctness, and created adversarial prompts for red-teaming to expose safety blind spots. Delivered second-pass audits to ensure quality consistency and drafted gold-standard exemplars to train new contributors. Maintained >98% accuracy and exceeded throughput benchmarks consistently.

Evaluated thousands of AI-generated responses for accuracy, clarity, fairness, and adherence to policy guidelines. Performed ranking tasks (RLHF) to improve reward model training, applied detailed rubrics to assess ambiguity, bias, and factual correctness, and created adversarial prompts for red-teaming to expose safety blind spots. Delivered second-pass audits to ensure quality consistency and drafted gold-standard exemplars to train new contributors. Maintained >98% accuracy and exceeded throughput benchmarks consistently.

2020 - 2024

Academic Linguistics & Annotation Research

MindriftTextEntity Ner ClassificationClassification

As part of graduate research in Language Technology, designed and applied annotation frameworks for syntax, semantics, and text classification. Built experimental rubrics to measure inter-rater consistency and applied hybrid rule-based/ML models to analyze annotated corpora. Delivered detailed reports on annotation quality and evaluator agreement that informed faculty research in computational linguistics. This academic foundation directly supports professional expertise in rubric-driven AI evaluation.

As part of graduate research in Language Technology, designed and applied annotation frameworks for syntax, semantics, and text classification. Built experimental rubrics to measure inter-rater consistency and applied hybrid rule-based/ML models to analyze annotated corpora. Delivered detailed reports on annotation quality and evaluator agreement that informed faculty research in computational linguistics. This academic foundation directly supports professional expertise in rubric-driven AI evaluation.

2017 - 2023

Multi-Modal Annotation – Text and Image Labeling

AppenImageClassificationQuestion Answering

Annotated and categorized large datasets of text and image content for search relevance, content moderation, and classification. Applied strict rubrics to ensure labeling consistency, flagged edge cases, and documented ambiguity to refine project guidelines. Conducted audits of contributor work, providing structured feedback that reduced error rates by 20%. Contributed to dataset quality that improved client-facing recommendation systems and content safety pipelines.

Annotated and categorized large datasets of text and image content for search relevance, content moderation, and classification. Applied strict rubrics to ensure labeling consistency, flagged edge cases, and documented ambiguity to refine project guidelines. Conducted audits of contributor work, providing structured feedback that reduced error rates by 20%. Contributed to dataset quality that improved client-facing recommendation systems and content safety pipelines.

2018 - 2020

Autonomous Vehicle Perception Annotation

CVATVideoBounding BoxPolygon

Labeled objects such as vehicles, pedestrians, and traffic signs in video and LiDAR datasets to train self-driving car perception systems. Used bounding box, polygon, and object-tracking techniques to ensure high-accuracy spatial annotations. Applied QA rubrics to validate scene classifications and performed audits on contributor annotations to reduce labeling inconsistencies. Improved dataset reliability that directly enhanced perception and safety models for autonomous vehicles.

Labeled objects such as vehicles, pedestrians, and traffic signs in video and LiDAR datasets to train self-driving car perception systems. Used bounding box, polygon, and object-tracking techniques to ensure high-accuracy spatial annotations. Applied QA rubrics to validate scene classifications and performed audits on contributor annotations to reduce labeling inconsistencies. Improved dataset reliability that directly enhanced perception and safety models for autonomous vehicles.

2017 - 2019

Education

Work History