Julia Goh
Founding Engineer (ML AI, Full Stack) at Unify
Malacca, Malaysia
Summary
🤩
Rockstar🎓
Top SchoolJulia Goh is a Founding Engineer specializing in ML/AI and full-stack development with five years of hands-on experience building end-to-end models, deployment pipelines, and production services. A UCL MEng graduate, she has led teams to create model hubs and transpiler features that unify PyTorch, TensorFlow, JAX and NumPy, and expanded an ML model collection from zero to 15+ in months for product showcases. Her work at PETRONAS delivered high-accuracy predictive pipelines for subsea corrosion and span estimation, and she has practical cloud deployment experience on Azure backed by Microsoft AI certifications (AI-900, AI-102, DP-900). An active contributor to the ivy project, she implemented core conversion and backend abstraction logic enabling ML code portability across frameworks—an indicator of deep compiler and systems-level understanding. Fluent in English, Chinese and Malay and based in Malacca, she blends research-quality rigor with product-focused execution and a knack for translating complex ML systems into usable developer and cloud tooling.
5 years of coding experience
3 years of employment as a software developer
GCSE A-Levels, 2A* 2A, GCSE A-Levels, 2A* 2A at Taylor's CollegeSijil Pelajaran Malaysia (SPM), 6A+ 3A, Sijil Pelajaran Malaysia (SPM), 6A+ 3A at SMK Infant Jesus ConventUniversity College London
Chinese, English, Malay, Japanese, Korean
Github Skills (8)
pytorch10
error-handling10
tensorflow10
jax10
back-end-development10
python10
api-design9
compiler-design9
Programming languages (4)
Github contributions (5)
ivy-llc/ivy
Apr 2022 - Jan 2023
Convert Machine Learning Code Between Frameworks
Role in this project:
Back-end Developer & Compiler Engineer
Back-end Developer & Compiler EngineerContributions:454 reviews, 266 commits, 632 PRs in 9 months
Contributions summary:Julia made significant contributions to the core conversion and framework abstraction logic of the Ivy library. Their commits focused on implementing and enhancing functions to convert machine learning code between various frameworks like Jax, PyTorch, and TensorFlow, including the addition of missing methods, updating backend attributes and fixing issues around keyword arguments. The contributions also involved refactoring of the internal API design related to exception handling and type compatibility, demonstrating a deep understanding of the library's architecture.
pythontensorflowframework-learningtemplatedata-science
juliagsy/juliagsy
Jun 2022 - Jan 2025
Contributions:5 pushes, 1 branch in 2 years 7 months
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