Staff Applied Machine Learning Engineer - Fraud & Abuse

You Will

• 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.

You Have

• 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.

Nice to Have

• 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.

Technologies We Use and Teach

• Python, Java, Kotlin, SQL.
• TensorFlow, PyTorch, XGBoost/LightGBM, embeddings, deep learning, and tree-based modeling ecosystems.
• Kafka or other event-streaming systems, batch data pipelines, feature stores, workflow orchestration, and model-serving systems.
• Cloud infrastructure, Kubernetes, data warehouses/lakehouses, monitoring, observability, coding agents, evaluation harnesses, and agent-assisted operations tooling.