Andrey Malevich
Member Of Technical Staff at OpenAI
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolAndrey Malevich is a seasoned technology leader and ML systems architect based in San Francisco with about eight years of experience delivering scalable AI platforms. As a Tech Director at Meta and now a Member of Technical Staff at OpenAI, he leads cross-functional teams building graph learning and large-scale recommendation technologies that power monetization and Ads systems. He has a proven track record of shipping production-grade ML infrastructure, from pioneering Caffe2 back-end work to modern graph-learning pipelines, including operator implementations, performance optimizations, and support for diverse data types. A hands-on contributor to open-source ML tooling, his work on the Caffe2 project emphasizes correctness, speed, and interoperability at scale. He studied computer science at Belarusian State University and data analysis at Yandex School of Data Analysis, combining rigorous academic training with practical, production-focused leadership.
9 years of coding experience
11 years of employment as a software developer
Computer Science, Computer Science at Belarusian State UniversityYandex School of Data AnalysisHigh School, Mathematics, High School, Mathematics at Belarussian State University Lyceum
Russian, English
Github Skills (16)
operator10
operators10
machine-learning10
caffe10
deep-learning10
back-end-development10
ai10
python10
caffe210
computer-engineering9
c-programming-language9
c-language9
performance-optimization8
tensorflow23
pytorch3
Programming languages (4)
Github contributions (5)
facebookarchive/caffe2
Nov 2016 - Feb 2018
Caffe2 is a lightweight, modular, and scalable deep learning framework.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:82 commits in 1 year 2 months
Contributions summary:Andrey primarily worked on fixing operator implementations within the Caffe2 deep learning framework, addressing issues such as handling empty batches and ensuring correct functionality of various operators like convolutions, pooling, and LRN. They also made improvements to serialization speed and addressed bugs related to parameter handling and shape inference, focusing on code related to the core framework functionality. Furthermore, the user contributed to enhancing the framework's capabilities by adding support for more data types and improving the handling of specific layer types, such as SparseToDense and Recurrent Networks.
pytorchscalablecaffe2deep-learningml
kennyhorror/pytorch
Oct 2018 - Sep 2019
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:9 pushes, 7 branches in 11 months
pythongpu-accelerationdeep-learninggpuacceleration
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