Mark

Key Skills

Software

Appen

Other

Top Subject Matter

Natural Language Processing

Llms Domain Expertise

Rlhf Domain Expertise

Top Data Types

Text

Image

Document

Top Task Types

Entity (NER) Classification

Classification

Red Teaming

Freelancer Overview

AI Data Quality Specialist. Brings 3+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Internal, Proprietary Tooling, and Appen. Education includes Bachelor of Science, Georgetown University (2021). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Entity (NER) Classification.

Labeling Experience

AI Data Quality Specialist

Text

As an AI Data Quality Specialist, I evaluated large volumes of LLM-generated text for accuracy, coherence, and alignment with human values. I contributed to RLHF prompt dataset creation and developed annotation guidelines to improve consistency among annotators. My work involved curating safety datasets, identifying problematic content, and mentoring junior team members. • Evaluated over 500 LLM outputs weekly using rubric-based assessments. • Created prompt–response pairs for RLHF data in STEM, law, and creative writing. • Improved inter-annotator agreement by 18% via refined guidelines. • Documented model hallucinations and edge cases for safety data curation.

2023 - Present

NLP Data Annotator

AppenTextEntity (NER) Classification

As an NLP Data Annotator, I performed entity extraction, text classification, and coreference resolution on text datasets for major enterprise clients. The projects included multilingual annotation, with tasks in both English and Spanish. I maintained high annotation accuracy and offered feedback to refine guidelines. • Completed text classification and entity recognition on diverse NLP projects. • Participated in multilingual annotation efforts. • Maintained a 98.5% quality score over 12 project cycles. • Provided feedback that improved company-wide style guides.

2021 - 2022

Research Assistant – Computational Linguistics

TextClassification

As a Research Assistant in Computational Linguistics, I contributed to the annotation of linguistic corpora for discourse and syntactic analysis. This role involved both manual and script-assisted labeling of raw text for academic research. I also supported the development of annotation tools and presented findings to the research community. • Annotated text corpora for discourse analysis and syntactic parsing. • Built scripts to clean and preprocess linguistic datasets. • Co-authored a poster on automated argument mining. • Supported faculty in research-related labeling activities.

2019 - 2021

Developer – Argument Quality Annotator Tool (Open Source Project)

OtherTextClassification

I developed an open-source GUI annotation tool for assessing the quality of arguments in news articles. This project included the creation and labeling of datasets for argument strength and evidence metrics. The tool was adopted by university labs as part of their annotation workflow. • Engineered a Python tool for argument quality annotation. • Labeled news article arguments for strength and evidence. • Supported academic research in computational argumentation. • Tool adopted by multiple university research groups.

Not specified

Project Lead – LLM Bias Detection Dataset (Personal Project)

TextRed Teaming

For the LLM Bias Detection Dataset project, I curated a dataset of adversarial prompts to uncover demographic and factual biases in open-source language models. The process combined prompt engineering and targeted data labeling techniques. Findings were shared publicly, supporting ongoing AI safety work. • Designed and labeled 2,000 adversarial prompt entries. • Focused on surfacing bias in open-source LLMs. • Shared outcomes with the AI safety community. • Work recognized by 3,000+ readers and experts online.

Not specified

Education

Work History