Oscar So
Staff Software Engineer at Meta
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolOscar So is a Staff Software Engineer in New York with nine years of experience building backend services, full-stack applications, and data-driven systems, currently advancing infrastructure and ads ML platforms at Meta. He combines production-grade service design with hands-on data science—having led teams at Cornell Data Science to build search and knowledge-graph platforms and contributed backend improvements to the well-known ConvoKit conversational analysis toolkit. Comfortable across cloud stacks (AWS/Azure), Python/Flask and React, Oscar has a track record of shipping scalable features, optimizing costs, and strengthening code maintainability. He mentors engineers and students alike, drawing on extensive TA experience and cross-disciplinary projects from AR-enabled mobile apps to enterprise React consoles. Known for rapidly learning new domains and pragmatically refactoring legacy code, he favors practical, testable solutions that improve developer ergonomics and system reliability.
9 years of coding experience
6 years of employment as a software developer
Master of Engineering - MEng, Computer Science, Master of Engineering - MEng, Computer Science at Cornell UniversityHigh School Diploma, High School Diploma at Hong Kong International School
English, Chinese, Chinese, French
Github Skills (10)
git-repository10
nlp10
conversation10
conversations10
repos10
python10
datasets9
machine-learning9
data-structures8
data-structure8
Programming languages (4)
Github contributions (5)
CornellNLP/ConvoKit
Jan 2022 - Jul 2022
ConvoKit is a toolkit for extracting conversational features and analyzing social phenomena in conversations. It includes several large conversational datasets along with scripts exemplifying the use of the toolkit on these datasets.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 release, 10 reviews, 40 commits in 5 months
Contributions summary:Oscar primarily focused on refactoring and improving the codebase within the `convokit` library. Their contributions included updating the code to use `iter_utterances` instead of accessing private variables, which likely improved code maintainability and data access. They also implemented new methods for adding speaker and utterances individually to the Corpus, enhancing the library's flexibility. Further work involved merging branches related to storage abstraction and code formatting, demonstrating efforts to improve code structure, organization, and maintainability.
nlpanalyzingconversationalconversational-aidataset
TusharK54/CS3110-Final-Project
Nov 2019 - Dec 2019
Contributions:73 commits, 69 pushes in 1 month
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