Summary
Jorge Tavares is a Principal Researcher with 11 years of experience applying AI and machine learning to production-scale search, recommendation and inference systems, currently driving research for Microsoft’s MSAI Recommendations and Brainwave FPGA inference efforts. He blends deep academic training (PhD in AI) and postdoctoral work with hands-on leadership at Microsoft and Volkswagen’s Data:Lab, repeatedly shipping architecture changes and neural ranking models that improved relevance and efficiency at scale. Jorge has led cross-functional teams as tech lead for search relevance, knowledge and conversation systems, and the Whole Page Relevance architecture, and has practical expertise porting and optimizing DNNs for low-latency FPGA inference. His background in evolutionary algorithms and complex systems informs a pragmatic, optimization-focused approach to hard engineering problems, and he has a track record of mentoring and supervising international research teams. Based in Munich, he is known for combining rigorous research methods with production-minded engineering to turn novel models into scalable, resource-efficient deployments.
11 years of coding experience
14 years of employment as a software developer
Complex Systems Summer School, Complex Systems Summer School at Santa Fe Institute
Doctor of Philosophy (Ph.D.) Informatics Engineering with specialization in Artificial Intelligence, Doctor of Philosophy (Ph.D.) Informatics Engineering with specialization in Artificial Intelligence at Universidade de Coimbra
Summer School on Evolutionary Computation, Summer School on Evolutionary Computation at EvoNet
English, Portuguese