Amazon’s Risk Preventionorganization constantly makes high judgment decisions across a wide breadth of constituents.
We build and deliver services for Buyers, Sellers, Vendors and other customers.
We ensure that Amazon is a safe and trustworthy place to shop and an amazing place to build a successful business selling products.
We support our many partners in this journey.
We face constant trade-offs and competing objectives that we need to appropriately balance.
We have to do all of this at high scale which only happens through technology and science.
You will be part of a team that uses technology including machine learning and graph modeling techniques, cloud computing services and highly available and scalable distributed systems that supports hundreds of millions of transactions across the globe.
We are the Core Relations Engineering team in Risk and Fraud Detection organization.
We provide highly effective, production ready relationship data insights (with transparency to audit, experiment, and enhance those insights) to allow teams across Amazon to make risk decisions.
Our clients are the scientists, engineers, product managers, and investigators behind 45+ programs across Amazon.
We provide all the necessary tools to engineer new features, explore graph-based insights, and detect risky relationships.
Come join the best in class team of engineers, who are owners, and inventors.
We are at the intersection of Machine Learning and Fraud Detection, while delivering customer impact at amazon scale.
Interact with ML scientists on a daily basis, and build the services to beat to keep the bad guys out.
Key job responsibilities
Develop large scale, high performance, high reliability ML systems for data, feature, model and rule management. -
Work closely with ML scientists, understand the requirements, design and deliver innovative next-gen solutions to help preserve customer trust.
Work as the owner of the system and operate excellent to provide stable and robust services to customers and other teams.
Innovate based on new ideas and build comprehensive system/features across the whole ML pipeline