Software Engineer - 2025 Internship Program (3 Months)

2 Months ago • All levels

Software Development & Engineering

Job Description

DataVisor is seeking a Platform Software Engineer Intern for a 3-month program. This role involves gaining hands-on experience in building a next-generation machine learning platform that integrates proprietary unsupervised machine learning (UML) technology with supervised machine learning (SML) algorithms. The team focuses on enhancing core detection algorithms and automating the training process to combat sophisticated fraud attacks. Responsibilities include designing and building machine learning systems for large datasets, utilizing machine learning techniques for fraud detection, building and optimizing systems and tools, designing distributed real-time systems, using big data technologies like Spark and Hadoop, and developing systems on real-time streaming technologies like Kafka and Flink. This internship offers potential for a full-time role.

Must Have:

Interest in fraud detection and machine learning
Computer science student nearing graduation or recent graduate
Experience with big data technologies (Spark, Hadoop, HBase, Cassandra)
Experience with real-time streaming technologies (Kafka, Flink)
Design and build ML systems
Use ML for fraud detection
Build and optimize systems and tools
Design distributed real-time systems

Add these skills to join the top 1% applicants for this job

saas-business-models

data-analytics

hbase

hadoop

spark

deep-learning

cassandra

algorithms

machine-learning

DataVisor is the world’s leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's solution scales infinitely and enables organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine, and investigation tools work together to provide guaranteed performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.

Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!

Summary

As a Platform Software Engineer Intern, you will gain hands-on experience in building a next-generation machine learning platform that integrates our proprietary unsupervised machine learning (UML) technology with various supervised machine learning (SML) algorithms. Our team focuses on enhancing core detection algorithms and automating the training process to stay ahead of increasingly sophisticated fraud attacks.

The Platform Team is responsible for developing the architecture that enables real-time UML, a critical component in fraud detection. We are seeking curious, creative, and eager-to-learn engineers to help expand our advanced streaming and database systems, which power our detection capabilities.

This position is ideal for computer science students who are nearing graduation or recent graduates with an interest in fraud detection and machine learning. It has the potential to lead to a full-time role following a three-month internship.

What you'll do with the team:

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)