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Eugene Yan is a Senior Applied Scientist at Amazon and an active investor in early-stage technology companies. He is renowned for his expertise in machine learning, artificial intelligence, and data science, contributing significantly to both Amazon's innovation and the broader tech startup ecosystem.
Eugene Yan primarily invests in early-stage technology companies. His focus areas include artificial intelligence (AI), machine learning (ML) infrastructure, data platforms, developer tools, and B2B SaaS solutions that leverage advanced technology to solve complex problems.
Eugene Yan works as a Senior Applied Scientist at Amazon. In this role, he is responsible for developing and deploying advanced machine learning and data science systems that enhance various Amazon services and drive technological innovation.
Eugene Yan is a Member of Technical Staff at Anthropic based in Seattle, Washington, where he focuses on developing field-frontier artificial intelligence systems. Prior to this role, he served as a Principal Applied Scientist at Amazon from 2020 to 2025, building recommendation engines and large language model products. His earlier career includes leading data initiatives as Vice President of Data at Lazada and working as a Data Scientist at IBM. He earned a Master of Science in Computer Science from the Georgia Institute of Technology and a bachelor's degree in psychology. Outside of his corporate roles, he authors a technical newsletter reaching over 11,800 readers and develops open-source prototypes like AlignEval and Obsidian Copilot. Yan currently focuses on engineering scalable machine learning applications and establishing practical design patterns for deploying reliable artificial intelligence in production environments.
Eugene Yan stands out as a prominent figure in the intersection of advanced technology and strategic investment. As a Senior Applied Scientist at Amazon, he brings a wealth of expertise in machine learning, artificial intelligence, and data science to one of the world's leading technology companies. His role involves developing and deploying sophisticated AI systems that power various Amazon services, contributing significantly to the company's innovation pipeline and customer experience.
Yan's career background is deeply rooted in the practical application of data science and machine learning. Before his tenure at Amazon, he honed his skills in various capacities, consistently focusing on building scalable and impactful data-driven solutions. This extensive experience has provided him with a unique perspective on the challenges and opportunities within the tech landscape, particularly in areas ripe for disruption through intelligent systems. He is widely recognized in the data science community for his insights into MLOps, machine learning system design, and the practicalities of bringing AI models from research to production.
As an investor, Eugene Yan leverages this deep technical understanding to identify and support promising early-stage startups. His investment focus areas are naturally aligned with his professional expertise: artificial intelligence, machine learning infrastructure, data platforms, developer tools, and B2B SaaS solutions that empower businesses with cutting-edge technology. He is particularly interested in companies that are building foundational technologies, improving the efficiency of data scientists and engineers, or creating novel applications of AI to solve real-world problems. Yan seeks out teams with strong technical founders who possess a clear vision for how their technology can scale and create significant market value.
While specific notable investments are not publicly detailed, Eugene Yan's involvement as an investor is driven by a desire to foster innovation in the sectors he knows best. He looks for companies that are not just developing new algorithms but are also focused on robust engineering, user experience, and a clear path to product market fit. His insights into the operational aspects of building and scaling AI-driven products make him a valuable advisor to the startups he supports, offering more than just capital but also strategic guidance derived from his extensive experience at Amazon and beyond. His commitment to advancing the field of machine learning extends from his daily work to his strategic contributions to the startup ecosystem.
