For employers

Hire this AI Trainer

Sign in or create an account to invite AI Trainers to your job.

Invite to Job
P

Poley Betsy

AI Data Specialist (Meta)

Expert

Key Skills

Software

No software listed

Top Subject Matter

LLM outputs
conversational AI
general and technical topics

Top Data Types

TextText
DocumentDocument

Top Task Types

No task types listed

Freelancer Overview

AI Data Specialist (Meta). Core strengths include Internal and Proprietary Tooling. Education includes Doctor of Philosophy, University of California, Berkeley (2024) and Master of Science, University of Washington (2020). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Expert

Labeling Experience

AI Data Specialist (Meta)

Text
Evaluated large language model (LLM) conversational responses for factual accuracy, reasoning validity, clarity, and tone. Performed large-scale pairwise comparisons, ranking tasks, and annotated strengths and weaknesses using standardized rubrics. Identified reasoning gaps and communication failures, ensuring high inter-annotator agreement following structured taxonomies. • Conducted detailed fact-checking using trusted public sources. • Produced consistent and reproducible evaluation artifacts for model quality improvement. • Supported reinforcement learning and model optimization workflows. • Maintained rigorous adherence to evaluation guidelines and benchmarks.

Evaluated large language model (LLM) conversational responses for factual accuracy, reasoning validity, clarity, and tone. Performed large-scale pairwise comparisons, ranking tasks, and annotated strengths and weaknesses using standardized rubrics. Identified reasoning gaps and communication failures, ensuring high inter-annotator agreement following structured taxonomies. • Conducted detailed fact-checking using trusted public sources. • Produced consistent and reproducible evaluation artifacts for model quality improvement. • Supported reinforcement learning and model optimization workflows. • Maintained rigorous adherence to evaluation guidelines and benchmarks.

2022 - 2024

Data Analyst / AI Data Contributor (Amazon)

Text
Reviewed AI-generated and structured outputs for logical coherence and content accuracy as part of data annotation and validation workflows. Applied statistical reasoning to detect inconsistencies and model errors, delivering qualitative judgments. Supported quality assurance processes for AI evaluation programmes across diverse subject areas. • Compared multiple outputs through fine-grained qualitative assessment. • Maintained strict performance standards in annotation tasks. • Provided data-driven feedback to enhance model output quality. • Contributed to systematic identification of outliers and model errors.

Reviewed AI-generated and structured outputs for logical coherence and content accuracy as part of data annotation and validation workflows. Applied statistical reasoning to detect inconsistencies and model errors, delivering qualitative judgments. Supported quality assurance processes for AI evaluation programmes across diverse subject areas. • Compared multiple outputs through fine-grained qualitative assessment. • Maintained strict performance standards in annotation tasks. • Provided data-driven feedback to enhance model output quality. • Contributed to systematic identification of outliers and model errors.

2019 - 2021

AI & Data Research Assistant (Private Research)

Text
Supported research-driven evaluation of experimental AI systems through structured annotation and validation. Developed annotation guidelines and conducted analytical validation using mathematical and logical frameworks. Participated in early-stage human-in-the-loop model assessment workflows. • Created structured annotation guidelines to ensure scoring consistency. • Utilized logical frameworks for output validation. • Improved feedback clarity for model assessment. • Assisted with the design of evaluation processes for research AI models.

Supported research-driven evaluation of experimental AI systems through structured annotation and validation. Developed annotation guidelines and conducted analytical validation using mathematical and logical frameworks. Participated in early-stage human-in-the-loop model assessment workflows. • Created structured annotation guidelines to ensure scoring consistency. • Utilized logical frameworks for output validation. • Improved feedback clarity for model assessment. • Assisted with the design of evaluation processes for research AI models.

2017 - 2019

Education

U

University of California, Berkeley

Doctor of Philosophy, Mathematics

Doctor of Philosophy
2020 - 2024
U

University of Washington

Master of Science, Applied Mathematics

Master of Science
2018 - 2020

Work History

No Work History added yet

Poley B. hasn’t added any Work History to their OpenTrain profile yet.