Posted 7mo ago

Software Engineering Manager - Platform Team

@ DataVisor
Mountain View, California, United States
$180k-$350k/yrHybridFull Time
Responsibilities:lead engineers, design platform, mentor team
Requirements Summary:BS in Computer Science or related field; MS/PhD preferred. 3+ years leading teams; 8+ years software development. Proficient in Java or C++, with Python; strong design/architecture skills. ML knowledge; experience with relational DBs, SQL, ORM; big data tech; Spring; strong communication.
Technical Tools Mentioned:Java, C++, Python, SQL, ORM, JPA, Hibernate, Spring, Spark, Flink, Cassandra, Kafka, Big Data, Distributed Systems, Machine Learning
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Job Description

DataVisor is a next-generation SaaS company that protects the world’s largest enterprises from fraud and money laundering. Our award-winning AI decision platform combines industry-leading unsupervised machine learning (UML) with advanced supervised models to stop fraudulent activity across financial transactions, mobile growth, social networks, and e-commerce.

We partner with leading global brands, delivering solutions built on top of our highly scalable platform. Behind these innovations is a world-class team of experts in big data, security, and distributed infrastructure, thriving in a culture that is open, collaborative, and results-driven.

Join us and help push the boundaries of what’s possible in fraud detection.

Role Summary

We are seeking a Software Engineering Manager to lead our Platform Engineering team. This team is at the heart of DataVisor’s detection capabilities, building the AI-based fraud and risk decision platform that powers real-time and batch unified decisioning at enterprise scale.

You will manage a talented team of engineers while also contributing to the design and development of our next-generation AI agent driven infrastructure. Together, you’ll enable our customers to detect and stop complex fraud patterns in real time.

What You’ll Do

  • Design & Build: Architect and deliver a large-scale, AI-based fraud and risk decision platform.
  • Innovate in Fraud Detection: Apply unsupervised, supervised, and agentic AI methods to uncover and stop fraudulent behavior.
  • Unify Decisioning: Drive the development of a real-time and batch unified decision platform that powers enterprise-scale fraud prevention.
  • Scale Infrastructure: Build and optimize distributed, real-time data systems for low-latency decisioning.
  • Leverage Big Data: Utilize Spark, Flink, Cassandra, and related technologies to enable high-throughput ML pipelines.
  • Lead & Mentor: Manage, coach, and grow a team of engineers, fostering technical excellence and professional development.