Vincent Kubicki is a Deep Learning Engineer with 12 years of experience applying advanced mathematics, numerical methods and ML to real-world problems across research and industry. He designs and optimizes C++ deep learning frameworks and GPU-accelerated kernels for computer vision, having implemented custom computational graphs, SIMD/CUDA optimizations and cuDNN integrations in production. His background spans PhD-level fluid mechanics, statistical modeling and reinforcement learning, enabling him to tackle both low-level performance challenges and high-level learning-efficiency problems. Comfortable moving models to microservice deployments and working directly with clients and researchers, he blends rigorous academic training with hands-on engineering. An occasional entrepreneur and consultant, he has a track record of building bespoke analysis pipelines for large-scale scientific and industrial datasets.
12 years of coding experience
14 years of employment as a software developer
Diplôme d'Ingénieur (postgraduate in engineering), Engineering Physics/Applied Physics, Diplôme d'Ingénieur (postgraduate in engineering), Engineering Physics/Applied Physics at Grenoble INP - Phelma
Preparatory classes, Mathematics, physics, Preparatory classes, Mathematics, physics at CPP- La prépa des INP
Master's degree, Statistics, Master's degree, Statistics at Université de Lille
Master's degree, Energy, Master's degree, Energy at Université Grenoble Alpes
PhD, Fluid mechanics and energetics, PhD, Fluid mechanics and energetics at IMT Lille Douai
Contributions:2 PRs, 29 pushes, 1 branch in 1 year 11 months
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