Summary

Experience

AI Training Lead|Anthropic

San Francisco, CAMar 2022Present

• Led a team of 10 AI Trainers to improve the safety and helpfulness of large language models, resulting in a 15% reduction in harmful outputs.
• Designed and implemented new annotation guidelines, increasing data consistency by 20% and accelerating model iteration cycles by 10%.
• Managed feedback loops with ML engineers, providing critical insights that led to a 5% improvement in model benchmark scores.
• Mentored junior trainers, streamlining onboarding processes and reducing ramp-up time for new hires by 25%.

Senior AI Trainer|Scale AI

San Francisco, CAAug 2019Feb 2022

• Annotated and validated over 100,000 data points for NLP and computer vision projects, contributing to a 90%+ data quality rating.
• Developed internal tooling and scripts that automated repetitive tasks, saving the team an estimated 15 hours per week.
• Collaborated with ML engineers to identify and resolve model biases, improving fairness metrics by an average of 12% across multiple datasets.
• Conducted quality assurance checks on external vendor data, reducing error rates by 30% and improving overall project timelines.

Projects

Prompt Optimization for E-commerce Chatbot

Jan 2023Apr 2023

• Developed and iteratively refined 200+ prompts for an e-commerce chatbot, improving customer query resolution rate by 18%.
• Conducted A/B testing on prompt variations, identifying optimal strategies that reduced chatbot hallucination errors by 10%.
• Documented a comprehensive prompt engineering guide, enabling future development teams to maintain high performance.

Custom Data Annotation Platform for Medical Imaging

Jun 2021Nov 2021

• Designed and implemented a web-based tool for medical image annotation, reducing labeling time by 25% for complex tasks.
• Integrated AI-assisted pre-labeling features, which accelerated the annotation process by an additional 15%.
• Enabled multi-user collaboration and real-time QA, increasing annotation consistency by 20% across diverse datasets.

Bias Detection and Mitigation in Language Datasets

Mar 2020Sep 2020

• Developed Python scripts to identify and quantify gender and racial biases in publicly available NLP training datasets.
• Proposed and implemented data augmentation and re-weighting strategies that reduced detected biases by up to 30%.
• Published findings on a personal blog, contributing to broader discussions on ethical AI development and fair data practices.

Education

University of California, Berkeley|Master of Science in Data Science

Berkeley, CASep 2017May 2019

• Achieved a GPA of 3.8/4.0, specializing in Machine Learning and Natural Language Processing.
• Coursework included Deep Learning, Statistical Modeling, and AI Ethics.
• Developed an NLP sentiment analysis model for customer reviews as part of capstone project.

University of California, San Diego|Bachelor of Science in Computer Science

La Jolla, CASep 2013Jun 2017

• Graduated Cum Laude with a GPA of 3.7/4.0.
• Honors Thesis: 'Optimizing Data Labeling Workflows for Enhanced Model Performance'.

Skills

AI/ML Tools

TensorFlowPyTorchHugging FaceLangChainOpenAI APIWeights & Biases

Data Annotation & Quality

Label StudioProdigyData ValidationGuideline DevelopmentQuality AssuranceInter-Annotator Agreement

Programming & Scripting

PythonSQLBashJupyter NotebooksPandasNumPy

NLP & NLU

Large Language Models (LLMs)Prompt EngineeringText ClassificationNamed Entity RecognitionSentiment AnalysisGenerative AI

Project Management & Collaboration

JiraConfluenceAgile MethodologiesTeam LeadershipCross-functional CollaborationStakeholder Communication