As platform engineers, we are building a next-generation machine learning platform, which incorporates our secret sauce, UML (unsupervised machine learning) with other SML (supervised machine learning) algorithms. Our team works to improve our core detection algorithms and automate the full training process.

As complex fraud attacks become more prevalent, it is more important than ever to detect fraudsters in real-time. The platform team is responsible for developing the architecture that makes real-time UML possible. We are looking for creative and eager engineers to help us expand our novel streaming and database systems, which enable our detection capabilities.

We continue to push the boundary of what's possible in fraud detection and data processing at scale. Join us to help usher in more innovative solutions to the fraud detection space.

What you'll do:

Design and build machine learning systems that process data sets from the world’s largest consumer services
Use unsupervised machine learning, supervised machine learning, and deep learning to detect fraudulent behavior and catch fraudsters
Build and optimize systems, tools, and validation strategies to support new features
Help design/build distributed real-time systems and features
Use big data technologies (e.g. Spark, Hadoop, HBase, Cassandra) to build large scale machine learning pipelines
Develop new systems on top of real-time streaming technologies (e.g. Kafka, Flink)

Requirements：
5+ years software development experience
5+ years experience in Java, Shell, Python development
Excellent knowledge of Relational Databases, SQL and ORM technologies (JPA2, Hibernate) is a plus
Experience in Cassandra, HBase, Flink, Spark or Kafka is a plus.
Experience in the Spring Framework is a plus
Experience with test-driven development is a plus