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DataVisor

Real-time fraud detection & risk management

Software Engineering Manager - Platform Team

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeMountain View, CA, USA$180k - $350k /yr

About the Job

Requirements

  • A Bachelor of Science degree in Computer Science or a related field is required.
  • Fluency in Java or C++, with knowledge of Python and hands-on experience in coding, system design, and architecture.
  • At least 3 years of experience leading or managing teams.
  • At least 8 years of software development experience.
  • A solid understanding of AI/ML concepts, including unsupervised, supervised, and emerging approaches such as agentic AI.
  • Experience applying machine learning in production systems is a strong plus.
  • Excellent oral and written communication skills.
  • A strong team spirit and collaboration mindset.

Responsibilities

  • Architect and deliver a large-scale, AI-based fraud and risk decision platform.
  • Apply unsupervised, supervised, and agentic AI methods to uncover and stop fraudulent behavior.
  • Drive development of a real-time and batch unified decision platform for enterprise-scale fraud prevention.
  • Build and optimize distributed, real-time data systems for low-latency decisioning.
  • Use Spark, Flink, Cassandra, and related technologies to enable high-throughput machine learning pipelines.
  • Manage, coach, and grow a team of engineers, fostering technical excellence and professional development.

Desired Qualifications

  • A Master of Science or PhD is preferred.
  • Familiarity with relational databases, SQL, and ORM frameworks such as JPA and Hibernate.
  • Experience with big data technologies such as Cassandra, Flink, Spark, and Kafka.
  • Knowledge of the Spring Framework.
  • Exposure to test-driven development and high-quality engineering practices.

About the Company

DataVisor

Real-time fraud detection & risk management

DataVisor offers a fraud detection and risk management platform for fintech and cybersecurity, helping financial institutions, e-commerce platforms, and online services detect and prevent fraudulent activity in real time. The core product integrates with clients’ existing data systems through APIs, supporting real-time or batch data processing to identify suspicious patterns. Machine learning analyzes large datasets to spot indicators of fraud and continuously improve detection accuracy. The company differentiates itself by providing a flexible, API-based integration that fits into clients’ current workflows and data environments, enabling real-time prevention across diverse use cases. Revenue comes from a subscription model that gives clients ongoing access to the platform and updates. The goal is to help organizations protect their operations and customers by staying ahead of increasingly sophisticated fraud schemes.

Company size
51-200
Company stage
Late Stage VC
Total funding
$94.5M
Headquarters
Mountain View, CA
Founded
2013

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