Staff Applied Machine Learning Engineer - Fraud & Abuse

You will design, build, and operate production machine learning decisioning systems that detect and prevent fraud and abuse across payments, accounts and marketplaces. You will work with ML modelers, risk analysts, product, compliance and operations to respond quickly to evolving abuse patterns while preserving access for legitimate customers. You will own the end to end lifecycle from data contracts to model deployment and monitoring, and you will help improve 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/risk domains such as payment fraud, identity/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