Amazon delivers tens of billions of packages to customers annually, which requires a large logistics setup. ML based systems could be leveraged to optimize the logistic network for improved customer experience. Some of the problems that could use an ML based solution include delivery date estimation, shipping cost estimation, and proactive identification of logistic defects. The data generated by the logistics system share a common theme of being structured in nature, which could be solved using a common ML framework. This talk will discuss how self-attention models are being used at Amazon to solve structured dataset problems in the logistics domain. It will also deep dive into the architecture choices made to deal with the peculiarities of logistics data at Amazon scale. Further, how our models improve over SOTA baselines is also presented.