Oliver Toh
Software Engineer at Amazon
United States
Summary
🤩
Rockstar🎓
Top SchoolOliver Toh is a software engineer with five years of experience building ML-infused systems and production software, currently contributing to Project Kuiper at Amazon. He brings a strong academic foundation in mathematics and computer science from Northeastern (3.86 GPA) and hands-on research experience analyzing connectomes and large longitudinal datasets at the Barabasi Lab. Oliver has practical ML deployment experience across industry internships and co-ops—from antibody engineering with LLM/XGBoost at Takeda to execution analytics at Arrowstreet—and has worked on ML library transpilation and frontend compatibility in open-source projects like ivy. His contributions to ivy include implementing activation functions, cross-framework math frontends, and rigorous tests, highlighting attention to correctness in numerical code. Comfortable in both research and production contexts, he also mentors and teaches (AI and calculus) and participates in community initiatives like the Northeastern Unicycle Club. Colleagues would describe him as a curious engineer who bridges theoretical rigor with pragmatic shipping.
5 years of coding experience
3 years of employment as a software developer
Bachelor of Science - BS, Mathematics and Computer Science, 3.86/4.0, Bachelor of Science - BS, Mathematics and Computer Science, 3.86/4.0 at Northeastern University
Chinese, English
Github Skills (11)
ivy10
pytorch10
machine-learning10
deeplearning-ai10
deep-learning10
python10
numpy9
jax9
tensorflow9
neural-network9
converter8
Programming languages (6)
Github contributions (5)
ivy-llc/ivy
Jul 2022 - Nov 2022
Convert Machine Learning Code Between Frameworks
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 55 commits, 40 PRs in 4 months
Contributions summary:Oliver contributed to the implementation of activation functions within the `ivy` library, specifically adding softplus methods to both array and container classes. They integrated tests, including tests for mathematical functions and numpy frontends, ensuring the library's functionality. Further work involved updating and correcting math functions, and implementing and testing torch methods, showcasing their focus on expanding and maintaining the library's machine learning capabilities.
pythontensorflowframework-learningtemplatedata-science
CerberusLatrans/ivy
Jul 2022 - Nov 2022
The Unified Machine Learning Framework
Contributions:90 pushes in 4 months
pythondata-sciencedeep-learningmachine-learningframework-learning
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