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Applied Scientist II, Physical Retail Technologies

Job ID: 1788934 | Services LLC


Job summary
Our group is developing advanced technologies that enhance the experience of shoppers in physical stores. Designed and custom-built by Amazon, existing products such as the Amazon Dash Cart and Amazon Go integrate a variety of advanced technologies including computer vision, sensor fusion, and advanced machine learning.

Key job responsibilities
As an Applied Scientist, you will research, implement and deploy scientific techniques that span the domain of Computer Vision, Machine Learning and Sensor Fusion. You will tackle challenging situations every day and have the opportunity to work with multiple technical teams at Amazon. You should be comfortable with a degree of ambiguity that’s higher than most projects and relish the idea of solving problems.

A day in the life
On a typical day, you will research on possible approaches in the literature for a given problem or implement Computer Vision/Machine Learning algorithms that demonstrates the feasibility of an approach, or implement the same in production. In addition, scientist also periodically apply for patents, give presentations on their research to the wider scientific community and expand their influence.

About the team
This is a fast growing computer vision, machine learning and research engineering team that continuously questions the status-quo of Customer and Merchant experience in Physical Stores, striving for disruptive innovation and defining the next generation of Physical Stores technology.


Masters or higher degree in Computer Science, Computer Vision or related field.
4+ years of non-internship professional development experience in developing Machine Learning and Computer Vision models.
2+ years of experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems


PhD in Computer Vision, Machine Learning, Statistics or related field.
4+ years of development experience of real-time machine learning/computer vision pipeline and infrastructure

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