Real-Time ML Platform Engineer for Fraud Detection

DataVisor; Mountain View (CA); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Health Insurance

401K

PTO

Job summary

DataVisor in Mountain View, CA is seeking platform engineers to build a next-generation machine learning platform that combines UML with supervised ML for real-time fraud detection. You will enhance core detection algorithms, automate training, and expand streaming and database capabilities to scale analytics.

Join a team delivering real-time UML with Spark, Kafka, and other big data tech to push fraud detection boundaries and support multiple business units with flexible architecture.

Qualifications

• 1-5 years software development experience.
• 1-5 years experience in Java, Shell, Python development.
• Relational databases, SQL and ORM technologies (JPA2, Hibernate) are a plus.
• Experience with Cassandra, HBase, Flink, Spark or Kafka is a plus.
• Experience with the Spring Framework is a plus.
• Test-driven development experience is a plus.

Responsibilities

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

Skills

Java

Python

Shell scripting

SQL

ORM (JPA/Hibernate)

Tools

Cassandra

HBase

Spark

Kafka

Flink

Spark/Flink

Spring Framework

Kubernetes

CUDA

Job description

DataVisor in Mountain View, CA is seeking platform engineers to build a next-generation machine learning platform that combines UML with supervised ML for real-time fraud detection. You will enhance core detection algorithms, automate training, and expand streaming and database capabilities to scale analytics.

Join a team delivering real-time UML with Spark, Kafka, and other big data tech to push fraud detection boundaries and support multiple business units with flexible architecture.