Staff Applied Machine Learning Engineer - Fraud & Abuse

📋 Description

• Design, build, and operate real-time and batch ML decisioning systems for fraud and abuse.
• Incorporate behavioral, graph, device, and event signals into models.
• Own production lifecycle for risk decisions, online/offline consistency, and rollout.
• Develop AI-assisted workflows for triage, investigation, and learning.
• Collaborate with modelers, product engineers, risk, compliance, and operations.
• Create scalable decision capabilities for product services and workflows.

🎯 Requirements

• 12+ years building and operating production software and ML systems.
• Deep expertise in fraud/risk domains (payments, identity, merchant risk).
• Strong production ML judgment across feature pipelines and serving.
• Judgment on false positives, noisy labels, and adversarial behavior.
• Experience with AI-assisted engineering tools with verification.
• Nice to have: graph-based fraud detection, embeddings, anomaly detection, or human-in-the-loop review.

🎁 Benefits

• Remote work options
• Medical insurance
• Flexible time off
• Retirement savings plans
• Modern family planning