With a master's degree in Computer Engineering and a PhD in Cyber-Physical Systems, specialized in Modular Robotics, I have spent over 16 years solving complex engineering challenges in robotics and embedded systems — bridging hardware, software, and mechanical engineering through scalable system architectures.
Throughout my career, I have authored more than 11 peer-reviewed publications and taught microcontrollers, embedded systems, real-time systems, and computer architecture at the Technical University of Graz. I also devised a precise torque sensor for which I hold an Austrian patent. Most recently, I spent six years serving as a System Integration & Validation Engineer and Robotics System Architect at NXP Semiconductors, where daily industry challenges allowed me to deeply apply my academic methodology at scale.
Why ML Engineering exists
Most companies don't waste resources because they lack talent—they waste them because nobody stepped back to evaluate the entire system before patching the immediate problem in front of them. ML Engineering exists to bridge the gaps between mechanical, electronic, and software domains, providing R&D teams with structured, expandable platforms instead of isolated fixes.
This approach delivers measurable business results, not just design philosophy. By replacing isolated layers with a leaner, systems-driven framework, projects can achieve up to a 60% reduction in production and maintenance costs, without sacrificing performance or long-term reliability.
How we work together
Depending on your project's specific goals, I integrate in one of three ways:
- 01Full project ownership — End-to-end technical responsibility running from initial architecture straight through deployment.
- 02Embedded alongside your team — Senior technical depth injected during critical phases to support and upskill internal engineers.
- 03On-call architecture advisor — A trusted second opinion when your current roadmap or engineering approach feels unnecessarily complex.