Alek Pikl is a Senior ML Engineer based in Munich with 10 years of engineering and research experience focused on efficient deep learning inference and quantization for custom hardware. Currently at Tensordyne, he architects GenAI inference pipelines and specializes in log-math data formats and low-precision model compression to maximize performance on specialized accelerators. His background spans academic research in active learning for semantic segmentation and practical systems work—ranging from embedded C++ tools and V2X digital twins to automation and QA tooling in Python. Alek combines hands-on implementation skills with algorithmic rigor from a Computational Science and Engineering master's at TUM, enabling him to translate novel numerical ideas into production-ready inference engines. He’s comfortable bridging research and product: designing hybrid genetic algorithms as an R&D engineer and shipping quantization techniques that make GenAI “go brrr.”
9 years of coding experience
4 years of employment as a software developer
Master's degree Computational Science and Engineering, Master's degree Computational Science and Engineering at Technical University of Munich
Manage and More by UnternehmerTUM
Semester abroad Electrical and Electronics Engineering, Semester abroad Electrical and Electronics Engineering at Tomsk Polytechnic University
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