Alexander Fritsler
Senior ML Engineer at X
Redwood City, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAlexander Fritsler is a Senior ML Engineer in Redwood City with nine years of experience building production recommender and RL-driven systems. He owns the core "For You" ranking at X and led a cross-functional transformer deployment that increased total user time by over 2%, and now applies that product-scale focus to search. Previously at Yandex and Samsung AI Center he shipped RL and model-compression work that measurably boosted engagement and efficiency (e.g., +2.5% listening time, 4x memory reduction), and has published research including a NeurIPS workshop paper. He co-authored and taught a practical Reinforcement Learning course and contributed a TRPO implementation to a widely used open-source RL curriculum. Trained in statistical learning theory, he blends rigorous research, hands-on engineering, and a knack for turning novel ML methods into scalable, impact-driven systems.
9 years of coding experience
5 years of employment as a software developer
Master's degree, Statistical Learning Theory, Master's degree, Statistical Learning Theory at Skolkovo Institute of Science and TechnologyMaster's degree, Statistical Learning Theory, Master's degree, Statistical Learning Theory at Higher School of Economics
Russian, English, German
Github Skills (7)
deep-reinforcement-learning10
pytorch10
deeplearning-ai10
deep-learning10
tensorflow10
reinforcement-learning10
keras9
Programming languages (2)
Github contributions (5)
yandexdataschool/Practical_RL
Mar 2017 - Mar 2022
A course in reinforcement learning in the wild
Role in this project:
ML Engineer
ML EngineerContributions:31 commits, 1 PR, 35 pushes in 5 years 1 month
Contributions summary:Alexander contributed to the implementation of a Trust Region Policy Optimization (TRPO) algorithm within a course on reinforcement learning. They added code related to a specific week's content, including the definition of a neural network, actions, rollouts, and loss functions, using TensorFlow. The user's primary focus was on building and integrating the core components of the TRPO algorithm, showcasing their understanding of policy optimization techniques.
pytorchgit-coursedeep-learningreinforcement-learningwild
Fritz449/DQN_Agent
Aug 2016 - Oct 2016
Contributions:31 commits, 30 pushes, 1 branch in 2 months
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