Staff Applied Machine Learning Engineer - Fraud & Abuse

Design, build, and operate production machine learning decisioning systems that detect and prevent fraud and abuse across payments, accounts, and marketplaces. Own the end-to-end lifecycle from data contracts to model deployment and monitoring while improving feedback loops and AI-assisted workflows for triage, investigation, and incident learning.

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 and risk domains such as payment fraud, identity and account integrity, merchant or marketplace risk, scams, trust and 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.

Benefits

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