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DESCRIPTION

Are you passionate about machine learning? Do you want to drive the innovation behind real-world recommendation systems? Do you want to impact what is shown to Amazon customers across the website by innovating with Amazon scale data? We are looking for a passionate engineer to help lead efforts on our centralized recommendation services team. We are growing our team in New York City (NYC) and looking for the right member to enhance and advance our machine learning services. Our team focuses on optimizing the customer experience on Amazon.com primarily through content recommendation and related optimizations of the customer. We operate a central service for ranking and optimization decisions as well as the backend infrastructure and algorithms to support these decisions. We are looking for a software developer with experience across the whole machine learning lifecycle to help us expand our ability to optimize for customers by developing and maintaining cutting-edge machine learning systems.

Key job responsibilities
* Push the boundaries of real-world recommendation and optimization systems
* Support science, engineering and product development on a scale only seen at Amazon.
* Obsess over customer needs and satisfaction.
* Create intellectual property, influence others while demonstrating significant creativity and being vocally self-critical.
* Architect for growth in the system’s content, use cases, and users.
* Operate hands-on and as an implementer of algorithms and models delivered to production systems.
* Work with partners to address concerns and incorporate subject matter expertise into our modeling efforts.

About the team
We are a high performing team that works on the full software life-cycle with a huge emphasis on diving deep and experimenting rapidly leveraging Amazon’s Machine Learning/Big Data expertise. We design, experiment, implement, test and iterate and our goal is to make the Amazon ecosystem experience uniquely personal. Our org (a.k.a P13N) revolutionized e-commerce with features such as “Customers Who Bought Also Bought” and “Recommended for You” and we are looking for creative and innovative engineers with a penchant for deep technical problem solving and the ability to lead and mentor other engineers across teams/divisions.