Embedded ML Compiler
Architected and built a custom machine learning compiler for embedded targets in Python 3, end to end in 5 months.
Engineering leader with 15 years of experience in embedded systems, IoT, and machine learning. Currently leading a team of 14 engineers at Mynaric in Munich.

I lead cross functional engineering teams building embedded and software products. My work spans architecture, delivery, and people: defining technical direction, running agile teams, owning DevOps and release processes, and hiring the engineers who make the rest of it work.
From 4G protocol stack development at Intel to leading a 14 engineer embedded team at Mynaric. The common thread is delivering complex software with the right team and process behind it.
A selection of engagements from across my career, delivered with the team and within the agreed timeline.
Architected and built a custom machine learning compiler for embedded targets in Python 3, end to end in 5 months.
Released the company's first real time trigger word ML model for embedded silicon in 4 months.
Designed and implemented OTA for the device fleet and owned the fleet management framework end to end.
Led the launch of a hardware/software product with 90 percent fewer reported issues than previous releases.
Restructured firmware projects on CMake and cut build times by 4x with zero release impact.
Coordinated full migration from Bamboo CI to Jenkins without missing a single SW release deadline.
A large part of my work is the team itself: hiring, growing tech leads, supporting engineers through difficult quarters, and creating the conditions for good technical decisions to be made.
Hired and onboarded engineering teams from the first engineer onward, defining roles, levels, and working practices as the team grew.
At Mr Beam, doubled the size of the software team while maintaining delivery cadence and individual performance.
Mentor engineers across seniority levels through structured 1:1s, technical reviews, and growth plans, including developing tech leads.
Work closely with product, hardware, QA, support, and customers so that engineering decisions reflect business and user context.
Run Scrum, Kanban, and Scrumban with OKRs and KPIs. Focus on ceremonies that produce decisions and retros that change behavior.
Set up Jira workflows, Confluence documentation, QA processes, release management, and recruiting pipelines that scale with the team.
Based in Munich and considering roles across Europe, particularly where embedded systems, IoT, ML, and modern product engineering intersect.