Summary
Daniel Bashir is an ML engineer and researcher with nine years of experience building production ML systems and compiler tooling at AWS, SambaNova, and OpenAI, and a background in applied science from Amazon’s personalization team. He combines theoretical work in learning theory and information-theoretic measures of model capacity with hands-on systems engineering (SageMaker AutoML, deep learning compilers) to bridge research and production. As Managing Editor of The Gradient and contributor to Skynet Today, he communicates complex AI and policy topics to broad technical and public audiences and has interviewed leading researchers. He’s also active in venture and media roles (Andreessen Horowitz, Susa, Clear Ventures), reflecting an interest in the intersection of technology, policy, and startups. A Harvey Mudd CS+Math alum who spent time studying at Peking University, he brings a rare mix of formal theoretical tools, production experience, and a longstanding curiosity about Chinese language and philosophical questions.
9 years of coding experience
7 years of employment as a software developer
Summer Study Abroad Electrical Engineering Chinese Language and History, Summer Study Abroad Electrical Engineering Chinese Language and History at Peking University
Bachelor’s Degree Mathematics and Computer Science, Bachelor’s Degree Mathematics and Computer Science at Harvey Mudd College
High School Liberal Arts and Sciences/Liberal Studies, High School Liberal Arts and Sciences/Liberal Studies at Phoenix Country Day School
English, Chinese