Summary
Lionel Peer is a Machine Learning Engineer and ETH Zürich MSc candidate in Electrical Engineering, focused on signal processing, generative modelling and representational learning at the intersection of physics and digital technology. He brings five years of engineering experience including two years scaling ML training and validation on distributed HPC systems, with strong Python and Julia skills and working knowledge of CUDA/C. At Lightly he applies practical ML expertise alongside solid software engineering practices—Docker, CI/CD, version control and Linux/database maintenance—to move research prototypes toward production. His academic work centers on inverse problems, giving him a physics-informed perspective on model design and robustness that complements his industry R&D experience at Bosch Japan on ADAS. Parallel to his civilian career he serves in the Swiss Armed Forces in space-focused officer roles, an uncommon background that sharpens systems thinking and operational leadership. Based in Zurich, he blends rigorous research training with hands-on system-level engineering for scalable ML solutions.
5 years of coding experience
1 year of employment as a software developer
Master of Science - MS, Electrical Engineering & Information Technology, Master of Science - MS, Electrical Engineering & Information Technology at ETH Zürich
Matura, SPF in Physik und angew. Mathematik, Matura, SPF in Physik und angew. Mathematik at Kantonsschule Kreuzlingen
German, English, French, Japanese