At eBay, we use state-of-the-art machine learning (ML), statistical modeling and inference, knowledge graphs, and other advanced technologies to solve business problems associated with massive amounts of data, much of which enters our system unstructured, incomplete, and sometimes incorrect. The use cases include query expansion and ranking, image recognition, recommendations, price guidance, fraud detection, machine translation, and more. Though most of the above use cases are common among other technology companies, there is a very distinctive and unique challenge that pertains only to eBay -- making sense of more than 1.3 billion listings, of which many are unstructured. Currently, we use our in-house machine learning solutions to approach this problem, but we also want to grow our community and future technologists that haven't had access to this type of data. By working with universities, we hope that it will pique academic curiosity within ML, spur more research in the ecommerce domain powered by a real-world ecommerce dataset, and help us improve our platform.