Block's Fraud and Abuse ML Engineering team needs to design, build, and evolve machine learning systems to safeguard the Block ecosystem from fraud, abuse, and other malicious activity by architecting and leading the development of high-scale, real-time ML systems that power fraud decisioning across the Block network.
Requirements
- Python, Java and Kotlin
- Tensorflow and PyTorch
- AWS, Databricks and Kubernetes
- MySQL, DynamoDB, Kafka, Spark
Responsibilities
- Build and maintain real-time and batch data pipelines/APIs to support ML model inference at scale.
- Design elegant ML pipelines and services, prototype new approaches, and productionize solutions at scale.
- Collaborate with product and engineering teams to define data models and schemas for consistent and structured data flow.
- Integrate and enrich diverse internal and third-party data sources to enhance our feature store and modeling capabilities.
- Ensure data quality and completeness through automated validation, monitoring, and alerting.
- Develop new triggers and event hooks that support enhanced risk evaluations and detections.
- Participate in SEV management by rapidly integrating new data, deploying new features, and implementing stopgap controls to mitigate risk.
Other
- 12+ years of experience in software development and demonstrated technical initiative and leadership on previous machine learning projects.
- A proven ability to shape how teams effectively adopt and evolve AI practices; driving adoption of AI-first workflows by coaching leaders, identifying scalable use cases, and embedding quality and accountability in team norms.
- Experience leading experimentation cycles and building a shared understanding of how AI drives business outcomes.
- Curiosity and a passion for Block's mission of economic empowerment.
- Experience helping your teammates grow through mentorship and providing constructive feedback.
