Alexander Zhipa
SDE III at Amazon
Bolton, England, United States
Summary
👤
Senior🎓
Top SchoolAlexander Zhipa is a senior software engineer with 14 years of experience specializing in backend systems and large-scale deep learning infrastructure. Currently an SDE III at Amazon, he architects and trains 100B+ transformer LLMs using parallelism strategies and productionizes training on native AWS hardware, contributing to DeepSpeed and other open-source tooling. His background spans finance-grade low-latency trading systems at Deutsche Bank, large consumer features for Alexa, and embedded/firmware-adjacent products at Samsung, demonstrating fluency across JVM, Python, and distributed systems. He holds advanced degrees in computer science and informatics theory and pairs rigorous academic training with hands-on ML engineering at scale. An engineer who moves smoothly between core systems design and cutting-edge model training, he’s comfortable pushing both infrastructure and model boundaries to deliver measurable impact.
14 years of coding experience
13 years of employment as a software developer
Master of Science Computer Science Applied Mathematics, Master of Science Computer Science Applied Mathematics at Peoples’ Friendship University of Russia
English, Russian, Ukrainian, Spanish
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (200)
language-model10
slurm10
sagemaker10
compression10
sequence-to-sequence10
neural-network10
nlp10
iteration10
tracer10
data-science10
embeddable10
amazon10
key-value-database10
intellij10
mixture-of-experts10
Programming languages (8)
Github contributions (5)
clumsy/DeepSpeed
Oct 2022 - Nov 2023
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Contributions:50 pushes, 10 branches in 1 year 1 month
pytorchdeepspeeddeep-learningeffectiveoptimization
clumsy/torchx
Oct 2023 - Mar 2025
TorchX is a universal job launcher for PyTorch applications. TorchX is designed to have fast iteration time for training/research and support for E2E production ML pipelines when you're ready.
Contributions:62 pushes, 30 branches in 1 year 5 months
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