Xinyi Wan is a machine learning researcher and engineer with a strong foundation in large-scale systems, distributed computing, and applied AI. He began his academic training at Zhejiang University, where he completed both his Bachelor’s and Master’s degrees in Computer Science. During this period, his early research and engineering exposure focused on scalability and distributed systems, including internships at Baidu and Google where he worked on database performance and large-scale infrastructure. His scholarly footprint—reflected in his Semantic Scholar and Google Scholar profiles—centers on machine learning systems, with an emphasis on practical, high-performance implementations of AI models in real-world environments.

Following his studies, Wan joined Google as a software engineer, contributing to large-scale ad serving systems—an experience that deepened his expertise in production-grade machine learning infrastructure. He then transitioned into more research-oriented roles, notably at FABU.ai, where he led system development for autonomous driving technologies. This work required integrating machine learning with real-time decision systems, further reinforcing his ability to bridge research and deployment. His career then accelerated at Sea (Shopee/Sea AI Lab), where he progressed from Senior Research Engineer to Senior Expert Engineer and led engineering efforts within Sea AI Lab (SAIL), focusing on applied AI at scale in consumer and platform contexts.

Before joining AMI Labs, Wan had built a hybrid profile combining academic research with deep industry execution. His work consistently sat at the intersection of machine learning theory and large-scale system design, with particular strengths in Python-based ML development and distributed architectures. Across his roles, he demonstrated a pattern of leading teams and building end-to-end AI systems—from foundational infrastructure to applied products—positioning him as a practitioner capable of translating cutting-edge research into scalable, production-ready solutions.