Tomas Kazmar is a Staff Machine Learning Engineer based in Vienna with 14 years of experience applying computer vision and ML to industrial and scientific problems. He has led R&D teams and shipped large-scale, production-grade systems—from a multi-modal part search serving catalogs of up to 12 million items to an image-based recognition pipeline scaled to ~70k classes that attracted major clients like Deutsche Bahn. His background blends academic rigor (PhD on regulatory code using CV/ML, publications in Nature and ICCV) with hands-on engineering in distributed training, dataset engineering, and runtime optimization that cut search latencies to sub-second and lowered operating costs. Comfortable bridging research, product and DevOps, he’s known for turning noisy, incomplete data into robust deployed services and mentoring teams to operationalize complex models. An interesting thread through his career is a persistent focus on scaling both algorithms and systems—whether for TB-scale 3D+t microscopy or millions-item industrial catalogs.
14 years of coding experience
14 years of employment as a software developer
Doctor of Philosophy - PhD Machine Learning and Computer Vision Group (Lampert), Doctor of Philosophy - PhD Machine Learning and Computer Vision Group (Lampert) at Institute of Science and Technology Austria
Erasmus stay Escuela Técnica Superior de Informática, Erasmus stay Escuela Técnica Superior de Informática at Universidad de Granada
Masters Faculty of Mathematics and Physics, Masters Faculty of Mathematics and Physics at Charles University
Contributions:33 commits, 1 push, 1 branch in 5 years
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Tomas Kazmar - Staff Machine Learning Engineer at Mimica