Staff Machine Learning Engineer, Fraud & Abuse

Block

About The Position

As a Staff Machine Learning Engineer in Fraud and Abuse ML Engineering, you will play a central role in designing, building, and evolving the machine learning systems that safeguard Block's ecosystem from fraud, abuse, and other malicious activity. You will architect and lead the development of high-scale, real-time ML systems that power our fraud decisioning across the Block network, protecting millions of customers and merchants while maintaining seamless user experiences.

Requirements

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.
A desire to be hands-on and help invent the future of AI.
The ability to work autonomously in the rapidly evolving world of ML.

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.
Apply ML and engineering best practices to shape how Block develops, tests, and maintains ML-platform solutions.
Support the integration of risk decisions and scores into downstream systems, ensuring they are consumed and acted upon correctly.

Benefits

Healthcare coverage (Medical, Vision and Dental insurance)
Health Savings Account and Flexible Spending Account
Retirement Plans including company match
Employee Stock Purchase Program
Wellness programs, including access to mental health, 1:1 financial planners, and a monthly wellness allowance
Paid parental and caregiving leave
Paid time off (including 12 paid holidays)
Paid sick leave (1 hour per 26 hours worked (max 80 hours per calendar year to the extent legally permissible) for non-exempt employees and covered by our Flexible Time Off policy for exempt employees)
Learning and Development resources
Paid Life insurance, AD&D, and disability benefits