Alex Rogozhnikov
Founding Engineer, AI Scientist, Protein Design at Chai Discovery
San Carlos, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAlex Rogozhnikov is an AI scientist specializing in protein design with 12 years of cross-disciplinary experience building ML systems at the intersection of biology and production engineering. Currently at Chai Discovery after leading data science and ML efforts as a founding engineer at Parallel Bio, he combines hands-on model engineering with startup execution to take projects from 0 to scale. His background spans applied physics and theoretical machine learning—PhD-level training and a Yandex School of Data Analysis masters—applied to problems from LHC particle tracking to in vitro brain-organoid phenotyping. An active open-source contributor, he is a core developer of the widely used einops tensor-manipulation library and has improved usability in toolkits like yandex/rep, reflecting a focus on readable, reliable scientific code. Colocated in San Carlos, CA, he brings both deep research chops and practical engineering discipline to protein design and scientific ML pipelines.
12 years of coding experience
9 years of employment as a software developer
Master's degree, Theoretical Physics, Master's degree, Theoretical Physics at Higher School of EconomicsMaster's degree, Machine learning & Data Science, Master's degree, Machine learning & Data Science at Yandex School of Data AnalysisLomonosov Moscow State University
Russian, English
Github Skills (9)
pytorch10
machine-learning10
xgboost10
tensor10
documentation10
numpy9
tensorflow9
jax8
flax6
Programming languages (17)
Github contributions (5)
arogozhnikov/einops
Sep 2018 - Jan 2023
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
Role in this project:
ML Engineer
ML EngineerContributions:22 releases, 14 reviews, 501 commits in 4 years 4 months
Contributions summary:Alex appears to be an ML Engineer, primarily focused on developing and refining the `einops` library for tensor manipulation. They are implementing and testing new functionalities for tensor operations, including reshaping and applying reductions, to be used in deep learning models. Their contributions involve modifications to the core `einops.py` file, and adding new tests and documentation in `tests.py`. The commits show that the user is adding support for features like support for grouping, oneflow support and code for testing.
jaxpytorchtensordeep-learningtensorflow
yandex/rep
Apr 2015 - Nov 2016
Machine Learning toolbox for Humans
Role in this project:Data Scientist
Data ScientistContributions:374 commits, 25 PRs, 256 pushes in 1 year 7 months
Contributions summary:Alex's commits primarily focused on modifying docstrings and parameter definitions within the "rep" repository, specifically concerning the "xgboost.py" and "tmva.py" files. These modifications suggest a focus on clarifying and enhancing the documentation for machine-learning models within the library. The changes included improving the clarity of descriptions for parameters like 'n_estimators', indicating a user involved in improving the usability and understanding of machine learning tools for humans.
machine-learning
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