Vahid Zehtab is a Senior Machine Learning Engineer based in Toronto with eight years of experience bridging academic research and product-focused ML R&D across generative models, computer vision, NLP, causal discovery and deep learning theory. He has led image-generative efforts at Stability AI and worked on latent diffusion for 3D vision and computational photography at Huawei, bringing research-grade probabilistic and generative modeling into applied systems. Vahid combines a mathematical, theory-first approach with hands-on engineering—shipping foundation models for histopathology, camera-quality generative pipelines, and tooling for deep learning at organizations from Vector Institute to Samsung Research. He actively moves ideas from papers to production, enjoys tackling novel inverse problems, and is exploring consulting and full-time roles that emphasize ML research and product impact. An unusual strength is his cross-domain fluency: comfortable with both the theoretical underpinnings of deep learning and the engineering required to deploy complex generative systems.
8 years of coding experience
6 years of employment as a software developer
Master of Science (M.Sc.), Applied Computing, Artificial Intelligence, Master of Science (M.Sc.), Applied Computing, Artificial Intelligence at University of Toronto
Bachelor of Science (B.Sc.), Computer Engineering, Bachelor of Science (B.Sc.), Computer Engineering at Sharif University of Technology
Contributions:2 releases, 65 commits, 29 pushes in 1 month
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