Konstantin Selivanov is a machine learning researcher and engineer with 15 years of experience building production ML systems, from billion-node graph analytics and search engines to perceptual prototypes that graduated Techstars. He specializes in representation learning, sequential and generative models (including transformers, state-space models, flows and diffusion), and practical memory and compression techniques, often pairing research rigor with low-level code optimization. Konstantin has led teams and research efforts as CTO and Data Science Lead, and now contributes research at 42 while drawing on early-stage product experience at startups backed by Sequoia and GV. A long-time Linux user and mathematician by training, he favors minimalism and efficiency in both models and engineering. Beyond papers and prototypes, he’s delivered tangible systems—transliterators, car-counting pipelines and large-scale search—that bridge research and deployment. His blend of deep theory, systems-level optimization and startup grit makes him adept at moving novel ML ideas into robust production.
15 years of coding experience
9 years of employment as a software developer
post-graduate, post-graduate at Moscow Institute of Physics and Technology (State University) (MIPT)
Specialist System Analysis Communications, Specialist System Analysis Communications at Southern Federal University (former Rostov State University)
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