Christopher Settles

Christopher Settles is a Machine Learning Engineer at Uber, specializing in building machine learning infrastructure to combat fraud and abuse on the platform. He has been with Uber since February 2021, focusing on developing systems that enhance the company's Risk Feature Store, which is critical for identifying and mitigating fraudulent activities across various services, including rides and Uber Eats.12

Professional Background

• Current Role: At Uber, Settles has designed a Change Data Capture (CDC) system for the Risk Feature Store, capable of handling over 200,000 write queries per second while optimizing storage costs and performance. His work has also involved productionizing machine learning models that have significantly reduced payment fraud losses.12
• Previous Experience: Before joining Uber, Settles interned at Google, where he developed security tools using Golang. He also held roles at Bank of America Merrill Lynch and Caterpillar Inc., gaining experience in data engineering and machine learning.1

Education

Settles earned his degree from the University of Illinois Urbana-Champaign, where he also served as a Head Teaching Assistant for a software design course, mentoring undergraduate students in coding and software development practices.1

Skills and Interests

Settles is proficient in various programming languages and technologies, including SQL, Python, and machine learning frameworks like PyTorch. His interests lie at the intersection of technology and human factors, particularly how software can positively impact society.

Overall, Christopher Settles exemplifies a strong background in both practical engineering skills and theoretical knowledge in machine learning applications within the tech industry.