Customer addresses, Gespatial information and Road-network play a crucial role in Amazon Logistics' Delivery Planning systems.
We own exciting science problems in the areas of Address Normalization, Geocode learning, Maps learning, Time estimations including route-time, delivery-time, transit-time predictions which are key inputs in delivery planning.
As part of the Last Mile Science & Technology organization, you’ll partner closely with other scientists and engineers in a collegial environment to develop enterprise ML solutions with a clear path to business impact.
We are actively looking to hire scientists at various levels to innovate and lead on these problem areas. Successful candidates will have deep knowledge of competing machine learning methods for large scale predictive modelling and natural language processing, the ability to graduate models to production, the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers, and the ability to take iterative approaches to tackle big, long term problems.
Here is a glimpse of the problem spaces and technologies that we deal with on a regular basis :
We make use of technologies like record matching, word embeddings, transformers, named entity recognition and semi-supervised learning for this problem.
This requires us to combine a variety of inputs (maps, delivery locations, defects) effectively, work in a multi-objective setting and exploit semantic as well as structural properties of places.
e., sequence of buildings and locations to visit. For these problems, we make use of multiple CV, Optimization (TSP) and Supervised learning techniques that can operate at scale.