Irina Khismatullina
Senior Software Engineer, Applied Machine Learning at Google
United Kingdom
Summary
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Senior🎓
Top SchoolIrina Khismatullina is a senior software engineer specializing in applied machine learning, with seven years of experience turning algorithmic research into production-grade systems. She currently applies ML at Google as a Senior Software Engineer, Applied Machine Learning, building end-to-end solutions that translate research into reliable real-world applications. Previously, at Jane Street, she focused on designing and automating reliable trading and post-trading systems, including observability tooling and production support. At Feebris, she led ML deployments enabling offline inference on low-end phones and developed signal-processing pipelines for medical sensor data, while establishing standards and CI-driven practices for faster, collaborative development. Her early work at source{d} spanned ML for code analysis, underscoring a versatile track record across finance, healthcare, and software engineering. She holds master's-level degrees in computer science and applied mathematics from MIPT and Yandex School of Data Analysis, pairing rigorous theory with hands-on delivery.
7 years of coding experience
6 years of employment as a software developer
Master's degree Applied Mathematics and Informatics, Master's degree Applied Mathematics and Informatics at Moscow Institute of Physics and Technology (State University) (MIPT)Master's-level degree, Computer Science, Graduate, Master's-level degree, Computer Science, Graduate at Yandex School of Data Analysis (Moscow)
Github Skills (30)
formatting10
typos10
analyzer9
code-reviews9
python9
autoencoder9
machine-learning9
tensorflow9
deep-learning9
neural-network8
data-science8
machine-learning-models7
csharp7
ml7
deep-neural-networks7
Programming languages (4)
Github contributions (5)
irinakhismatullina/style-analyzer
Jul 2018 - Aug 2018
Style analyzer experiments
Contributions:25 pushes in 27 days
analyzer
irinakhismatullina/models
Mar 2019 - Jul 2019
Machine learning models for MLonCode trained using the source{d} stack
Contributions:23 pushes, 1 branch in 4 months
autoencoderpythondata-scienceneural-networksmachine-learning
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