职位来自于拉勾

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