Julia Shuieh

Hi, I'm Julia.

I'm currently a Machine Learning Research Engineer at Scale AI and a Master's student at Georgia Tech for their MSCS program. I graduated from Carnegie Mellon's Artificial Intelligence program in 2022. I aspire to advance technology and our understanding of ourselves by exploring the intersection of artificial intelligence and psychology. I also wish to dive into the unlimited possibilities of computer science, and bring out others' potential through mentorship and support.

Aside from my current experience, I have interned at Meta, Snap, Roblox, and VMware, where I have tackled various machine learning, internal tooling, and automated testing problems. I also have done research at CMU for VR and HCI, various teaching roles for CS and AI topics, and extracurricular projects for robotics and game development. My diverse background and set of skills allows me to tackle all types of projects and problems.

Education

August 2022 - May 2024

Georgia Institute of Technology

Master's of Science: Computer Science

Specialization in Machine Learning
Cumulative GPA: 4.00 / 4.00
Coursework: Machine Learning, AI for Robotics, Knowledge-Based AI

August 2019 - May 2022

Carnegie Mellon University

Bachelor's of Science: Artificial Intelligence

Cumulative QPA: 3.87 / 4.00
Coursework: AI & ML, Deep Learning, Computer Vision, Robotics, Human-Centered Software
Extracurriculars: CoEx Lab, Augmented Perception Lab, SASE, Society of Women Engineers

Minor in Psychology

Coursework: Origins of Intelligence, Cognitive & Social Psychology, Experimental Design

August 2015 - May 2019

Cupertino High School

High School Diploma

Unweighted GPA: 4.00 / 4.00
Honors: Class Valedictorian, National AP Scholar, Gold Award
Extracurriculars: FIRST FRC Team 2473, Cupertino Game Dev Club, Mixed Martial Arts

Work Experience

Machine Learning Research Engineer - Scale AI

Jan 2023 - Present

Software Engineer Intern - Snap

Aug 2022 - Dec 2022

Migrated feature importance workflow to new internal ML framework, allowing more flexibility with input datasets while reducing cost by 67%

Created new analysis workflow to pinpoint features causing model scoring issues in production

Advised future model design for ad auction and opt-out users through feature importance experiments

Software Engineer Intern - Meta

May 2022 - Aug 2022

Improve model accuracy by 4% by compressing user features to utilize 1700+ features without large costs

Developed offline experiment workflow for new features to provide faster experiment results and more flexibility

Conducted thorough experiments and analysis to maximize gains and suggest further improvements

Productionized and began A/B testing for ranking models with new embedding features