Summary
Zeid Solh is a Machine Learning Engineer with 16 years of hands-on experience blending hardware and software expertise to deliver production-ready ML solutions. Currently at UiPath, he upgraded multiple Kubeflow pipelines to v2, improving maintainability and execution efficiency for enterprise ML workflows. His recent work includes privacy-first deployments—running a local Mixtral 8x7B LLM to replace API calls—and optimizing Kubernetes pipelines by removing PVCs to simplify data handling. Trained at UCLA and UC Berkeley (MEng EECS), Zeid pairs academic rigor with practical engineering across computer vision, ML infra, and backend APIs. He brings a track record of shaving operational overhead and improving system reliability, and is comfortable moving between low-level C/C++ and high-level ML tooling. Based in Dallas, he combines startup agility with enterprise discipline to turn complex research into scalable products.
16 years of coding experience
Master of Engineering - MEng, Electrical Engineering and Computer Sciences, Master of Engineering - MEng, Electrical Engineering and Computer Sciences at University of California, Berkeley
High School Diploma, High School/Secondary Diploma Programs, High School Diploma, High School/Secondary Diploma Programs at International College
University of California, Los Angeles
Summer Courses, Computer Science, Summer Courses, Computer Science at Phillips Academy
French, Arabic, English