I engineer the systems and infrastructure for applied machine intelligence.

I design and engineer intelligent systems with a focus on reliability, scalability, and clear architecture. My work centers on backend model orchestration, distributed training infrastructure, and the practical integration of statistical learning into production software. I’m fluent in Go and Python, and I enjoy building systems that translate complex research into tools that feel predictable, observable, and maintainable.

My background spans full-stack development, applied machine learning, and systems design, which gives me a broad perspective on how intelligent software behaves end-to-end. I’m motivated by technical clarity and understanding a system deeply enough to make it simpler, faster, and more expressive.

Beyond pure engineering, I’m interested in the craft of interaction: how people experience intelligent systems, how digital spaces can evoke real places, and how technology can support creativity. I often explore this through visual work: experimenting with scene-building, stylized environments, and the intersection of software architecture and design.

I’m also drawn to large-scale questions about intelligence and systems: how they emerge, how they break, and how they evolve under constraints. Topics like the Fermi paradox, complex systems, and the physics of high-speed environments inform how I think about the future of AI and the infrastructures that will support it.

Above all, I like building things that feel solid via systems with clean boundaries, clear data flow, and room to grow. Whether I’m architecting a training pipeline, assembling a model-serving layer, or prototyping a creative environment, I approach the work the same way: understand the system, refine the interfaces, and deliver something elegant and robust.