Building Scalable ML Systems: Lessons from the Trenches
Key insights from years of building and deploying machine learning systems at scale, including common pitfalls and best practices.

Problem Solver | Engineering Director | Former CS Lecturer | Dad in Debug Mode
I'm Sunsern Cheamanunkul, an Engineering Director and hands-on engineer with a PhD in Computer Science and a background as a Computer Science lecturer. Over the past years I've worked at the intersection of machine learning, scalable system design, and engineering leadership, helping teams turn research ideas and prototypes into reliable, production-grade systems.
I enjoy working with teams on problems that sit between research and engineering: designing architectures for ML systems, building platforms that make experimentation safer and faster, and mentoring engineers as they grow into technical leaders. Previously, I taught computer science at the university level, which shaped how I communicate complex ideas and how I think about learning and teaching in engineering teams.
These days, my focus is on building AI-powered products that actually deliver value in the real worldbalancing correctness, scalability, and iteration speed. Outside of work, I'm often in "dad in debug mode", applying the same curiosity and patience to parenting that I bring to engineering.

Key insights from years of building and deploying machine learning systems at scale, including common pitfalls and best practices.
Reflections on transitioning from a CS lecturer to an engineering director, and what I learned along the way.
Practical strategies for building high-performing engineering teams, from hiring to fostering a culture of excellence.