I'm a software engineer with a background in biochemistry and a Ph.D. in Biophysics from ETH Zürich. I build the computational infrastructure, open-source scientific software, and backend systems that researchers rely on to work with complex data. Increasingly, that work is about making scientific tools legible to AI: I build Model Context Protocol (MCP) layers and retrieval systems that give AI assistants and developers structured access to what a tool can actually do — its inputs, outputs, and parameters — so analyses are easier to compose, automate, and reproduce.
Currently, at ETH Zürich, I contribute to the engineering of production-grade open-source scientific software, primarily within the Rachis (formerly QIIME 2) bioinformatics ecosystem. In addition to contributing to core frameworks, my work has focused on helping establish engineering standards around testing, documentation, thorough code review, CI/CD, and long-term maintainability, alongside supporting junior developers and collaborating across institutional stakeholders.
I am interested in software engineering roles involving backend systems, developer tools, data-intensive systems, scientific computing, open-source ecosystems, and ML-adjacent infrastructure—especially in environments that value high code quality, developer productivity, and scientific rigor.
