Staff Applied Machine Learning Engineer - Fraud & Abuse

Block builds simple, powerful tools that make progress towards an economy that’s truly open to all. As a Staff Applied Machine Learning Engineer focused on Fraud & Abuse, you will design, build, and operate production ML decision systems that reduce payment fraud and other adversarial activity across Block. You will collaborate with various teams to optimize reliable decisions while preserving access for legitimate customers.

Responsibilities:

• Build and operate real-time and batch ML decisioning systems for payment fraud, scams, identity and account integrity, merchant and marketplace risk, and abuse prevention
• Integrate behavioral, graph, device, network, event-stream, and third-party signals into low-latency model serving, decision APIs, and product controls
• Own the production lifecycle for risk decisions, including data contracts, feature quality, online/offline consistency, monitoring, drift detection, safe rollout, rollback, and incident response
• Develop feedback loops and verified AI-assisted workflows for triage, investigation support, alert clustering, graph exploration, simulation, and post-incident learning
• Partner with modelers, analysts, product, compliance, and operations to balance fraud losses, customer access, false positives, product velocity, support burden, and long-term trust
• Create reusable decision and evaluation capabilities that product services, internal tools, and AI-assisted workflows can safely consume

Requirements:

• 12+ years building and operating production software and ML systems for business-critical products
• Deep expertise in fraud/risk domains such as payment fraud, identity/account integrity, merchant or marketplace risk, scams, trust & safety, abuse prevention, or compliance decisioning
• Strong production ML judgment across feature pipelines, model serving, evaluation, monitoring, low-latency integration, safe rollout, and incident response
• Sound judgment around false-positive tradeoffs, noisy labels, adversarial behavior, customer harm, and cross-functional decisions
• Experience using AI-assisted engineering tools with appropriate verification, testing, and review for high-stakes systems
• Experience with graph-based fraud detection, behavioral sequence models, embeddings, entity resolution, anomaly detection, or human-in-the-loop review
• Experience building fraud operations tooling for triage, case management, alert clustering, graph exploration, or policy simulation
• Experience with regulated financial services, model governance, auditability, explainability, or decision logging