Fedor Kitashov is a London-based Research Engineer at Google DeepMind with nine years of applied ML and computer vision experience across industry and research. He has led motion prediction for self-driving trucks at Ozon and built production-grade generative AI and latency-aware NAS systems at Snap that powered high-impact lenses and doubled low-end Android FPS. His background blends deep learning research (MIPT, Yandex School of Data Analysis) with hands-on systems work from Kubeflow/TPU infra at CERN to real-time perception pipelines for autonomous vehicles. Fedor’s work regularly moves models from prototype to production, including large-scale image restoration and colorization projects and an open-prompt image-to-image diffusion model that outperformed leading baselines. He holds a UK Global Talent visa and combines academic rigor with pragmatic engineering—an ability underscored by building sensor-placement strategies and delivering ML roadmaps presented to C-suite stakeholders. Colleagues describe him as a researcher who codes like an engineer and leads like a practitioner, bridging novel algorithms with measurable product impact.
9 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at Russian Oil and Gas Research Institute VNII NEFT
Bachelor's degree, Applied Mathematics and Physics, Bachelor's degree, Applied Mathematics and Physics at Moscow Institute of Physics and Technology (State University) (MIPT)
Master's degree, Deep Learning Research, Master's degree, Deep Learning Research at Yandex School of Data Analysis
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Fedor Kitashov - Research Engineer at Google DeepMind