Description

　　We are looking for talented Applied Scientists who are adept at a variety of skills including the application of large language models, development of computer vision models, and the development of headcount forecasting models.

　　Our mission is to: a) improve the reliability of equipment (conveyance, motors, robotics), and b) provide recommendations on required headcount capacity for sites to complete the forecasted work. To improve equipment reliability, we need to effectively identify from sensors, images, and video specific actions on material handling equipment (MHE) that can prevent unplanned downtime. With millions of products available on Amazon.com comes variation in weight, size, material, and shape. We build products and systems to detect and prevent equipment downtime using a diverse set of classification and anomaly detection algorithms including LLMs. We are still day 1 and have an exciting roadmap to build AI predictive maintenance models, deploy scalable causal inference solutions to measure the impact of events, and optimize the reliability of conveyance helping Amazon scale for years to come.

　　As a Senior Applied Scientist, you will design, develop, and maintain scalable Forecasting and Artificial Intelligence models with automated training, validation, monitoring, and reporting. You will work closely with other scientists and engineers to architect and develop new learning algorithms and prediction techniques. You will collaborate with product managers and engineering teams to design and implement scientific solutions for Amazon problems. Provide technical and scientific guidance to your team members. Contribute to the research community, by working with other scientists across Amazon and publish papers at peer reviewed journals and conferences.

　　Key job responsibilities

　　Design and implement scalable infrastructure that enables stacked deep learning models to detect a variety of defects in fractions of a second;

　　Design and implement anomaly detection and large language models to identify defects associated with customer packages;

　　Design and implement capacity forecasting models;

　　Experiment and scale models to thousands of sites worldwide;

　　Collaborate with RME internal and external stakeholders and have a cross-team impact;

　　Create and share with audiences of varying levels technical papers and presentation.

　　About the team

　　We are a growing team of applied, research, and data scientists working together with an engineering team and product managers to create the next-generation IIoT platform for the Reliability and Maintenance Engineering org.

　　We are open to hiring candidates to work out of one of the following locations:

　　Luxembourg, LUX

　　Basic Qualifications

　　Proven experience building machine learning models for business application

　　PhD, or Master's degree and significant applied research experience

　　Experience programming in Python, Java, C++, or related language

　　Experience with neural deep learning methods and machine learning

　　Preferred Qualifications

　　Experience with modeling tools such as R, or libraries such as scikit-learn, anomalib, Spark MLLib, MxNet, Tensorflow, numpy, scipy, etc.

　　Experience with large scale distributed systems such as Hadoop, Spark etc.

　　Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.