Bradley Crawford

Key Skills

Software

AWS SageMaker

Appen

Clickworker

CloudFactory

CrowdSource

Data Annotation Tech

Dataturk

Google Cloud Vertex AI

Labelbox

Label Studio

Mindrift

OneForma

Remotasks

Scale AI

Snorkel AI

SuperAnnotate

Surge AI

Toloka

Telus

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Text

Top Task Types

Evaluation Rating

Fine Tuning

Prompt Response Writing SFT

Text Generation

Translation Localization

Freelancer Overview

I have extensive experience in data labeling and AI training data, spanning roles at leading organizations like Outlier, OpenAI, and Scale AI. At Outlier, as a Data Annotator & Quality Assurance Specialist from September 2022 to August 2023, I annotated and validated complex datasets in mathematics, finance, and logical reasoning for LLM fine-tuning, ensuring accuracy and consistency through domain expertise in optimization and operations research. I conducted quality assurance audits, provided feedback to refine workflows, and collaborated with AI researchers on task guidelines for mathematical reasoning and code evaluation. Prior to that, as an AI Research Fellow at OpenAI from June 2021 to September 2022, I built and annotated mathematical datasets to train LLMs in symbolic reasoning and problem-solving, leading workflows in optimization, probability, and number theory while reviewing AI-generated proofs and implementing feedback loops. Additionally, at Scale AI from July 2020 to February 2021, I served as an AI Ethics Expert & Response Reviewer, auditing LLM outputs for safety, bias, and factual accuracy in quantitative domains, leading ethics assessments, and contributing to model alignment through human-in-the-loop training. What sets me apart is my PhD in Pure and Applied Mathematics from Princeton University, combined with deep expertise in computational modeling and operations research, enabling precise annotation of high-dimensional, technical datasets.

Labeling Experience

Data Annotator & Quality Assurance Specialist

Scale AITextText GenerationRLHF

• Annotated and validated complex datasets in mathematics, finance, and logical reasoning domains for LLM fine-tuning. • Applied domain expertise in optimization and operations research to ensure annotation accuracy, consistency, and relevance. • Conducted quality assurance audits and provided performance feedback to improve labelling workflows and model understanding. • Collaborated with AI researchers to refine task guidelines for mathematical reasoning and code evaluation.

• Annotated and validated complex datasets in mathematics, finance, and logical reasoning domains for LLM fine-tuning. • Applied domain expertise in optimization and operations research to ensure annotation accuracy, consistency, and relevance. • Conducted quality assurance audits and provided performance feedback to improve labelling workflows and model understanding. • Collaborated with AI researchers to refine task guidelines for mathematical reasoning and code evaluation.

2022 - 2023

AI Research Fellow

Scale AIDocumentEntity Ner ClassificationAction Recognition

• Built and annotated mathematical datasets for training LLMs in symbolic reasoning and problem solving. • Led design of problem-solving workflows in optimization, probability, and number theory. • Reviewed AI-generated proofs and structured feedback loops to refine neural reasoning capabilities.

• Built and annotated mathematical datasets for training LLMs in symbolic reasoning and problem solving. • Led design of problem-solving workflows in optimization, probability, and number theory. • Reviewed AI-generated proofs and structured feedback loops to refine neural reasoning capabilities.

2021 - 2022

AI Ethics Expert & AI Response Reviewer

Scale AITextQuestion AnsweringText Generation

• Reviewed and audited LLM-generated content for safety, bias, hallucination, and factual correctness within scientific and mathematical domains. • Led ethics compliance assessments focusing on fairness and truthfulness of AI outputs across finance, logic, and applied science contexts. • Flagged and corrected ambiguous or misleading outputs, improving model safety layers through human-in-the-loop training. • Participated in internal forums on model alignment, transparency, and responsible AI development.

• Reviewed and audited LLM-generated content for safety, bias, hallucination, and factual correctness within scientific and mathematical domains. • Led ethics compliance assessments focusing on fairness and truthfulness of AI outputs across finance, logic, and applied science contexts. • Flagged and corrected ambiguous or misleading outputs, improving model safety layers through human-in-the-loop training. • Participated in internal forums on model alignment, transparency, and responsible AI development.

2020 - 2021

Education

Work History