Reinis Veips
Owner at Imprimus
Riga, Vidzeme, Latvia
Summary
👤
Senior🎓
Top SchoolReinis Veips is an embedded systems and avionics engineer with 13 years of hands-on experience building firmware, control and stabilization systems for UAVs, and machine vision and industrial automation solutions. As founder of Imprimus and former CTO/R&D lead at Atlas UAS, he blends electronics, CNC machining and software to solve problems where hardware and code intersect. His work spans low-level embedded firmware, control algorithms, and systems design, with production avionics experience from Frankenburg Technologies and robotics control software developed at Ubiquiti Networks. Reinis also contributes to open-source C++ ML tooling—helping improve tiny-dnn’s Caffe converter and BatchNorm weight handling—which reflects a practical cross-disciplinary fluency in embedded, control, and machine learning stacks. Based in Riga, Latvia, he pairs a Master’s in Computer Systems Analysis with a builder’s mentality: if electronics aren’t enough, he’ll reach for the mill.
13 years of coding experience
13 years of employment as a software developer
Master's degree, Computer Systems Analysis/Analyst, Master's degree, Computer Systems Analysis/Analyst at Rīgas Tehniskā universitāte (Riga Technical University)Madona State GymnasiumBachelor's, IT, Bachelor's, IT at RTU
Github Skills (7)
neural-network10
machine-learning10
c-language10
deep-learning10
batch-normalization10
cprogramming-language10
unit-testing9
Programming languages (7)
Github contributions (5)
tiny-dnn/tiny-dnn
Dec 2016 - Mar 2017
header only, dependency-free deep learning framework in C++14
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 5 PRs, 9 comments in 3 months
Contributions summary:Reinis primarily contributed to the C++ deep learning framework by implementing features for the Caffe converter. This included adding support for ELU activation, skipping HDF5Data layers, and loading weights for BatchNorm layers. Furthermore, the user fixed issues with loading weights, particularly for MinGW-w64 builds on Windows, and added tests to verify BatchNorm weight loading.
deep-learningdependency-freec-plus-plusmachine-learningneural-network
festlv/carpc
Jan 2013 - Sep 2014
Contributions:53 commits in 1 year 8 months
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