The Adobe Risk Platform (ARP) is Adobe's centralized fraud prevention and risk-decisioning platform that protects surfaces like Commerce, Stock, and Firefly with real-time decisions that minimize impact on the customer experience. As a Senior Software Engineer focused on Data Science and Platform Engineering, you will build the ML models that detect and mitigate fraud and abuse and work on building the low-latency platform that serves them at scale across Adobe's most sensitive, highest-volume surfaces. What you'll do: Own the ML lifecycle end-to-end — data engineering, offline model development, and real-time inference — turning behavioral, device, and transaction signals into features that sharpen detection. Track precision, recall, false positives, and business impact, and experiment with new approaches (including LLMs and agentic AI) to catch more while reviewers do less. Own the platform that serves those models in production, from APIs and SDKs to feature stores and the integration points that make onboarding new surfaces turnkey. Keep it fast, reliable, and scalable as usage grows. Partner with fraud investigators, Trust & Safety, and platform teams to turn new fraud patterns into new defenses, fast. What you need to succeed: Bachelor's or Master's in Computer Science, Statistics, ML, or related — or equivalent experience. Deep experience with ML techniques — supervised/unsupervised learning, anomaly detection, ensembles — applied to fraud or risk detection. 5+ years building production ML systems and scalable backend services. Comfortable owning a model's full lifec

The Adobe Risk Platform (ARP) is Adobe's centralized fraud prevention and risk-decisioning platform that protects surfaces like Commerce, Stock, and Firefly with real-time decisions that minimize impact on the customer experience. As a Senior Software Engineer focused on Data Science and Platform Engineering, you will build the ML models that detect and mitigate fraud and abuse and work on building the low-latency platform that serves them at scale across Adobe's most sensitive, highest-volume surfaces. What you'll do: Own the ML lifecycle end-to-end — data engineering, offline model development, and real-time inference — turning behavioral, device, and transaction signals into features that sharpen detection. Track precision, recall, false positives, and business impact, and experiment with new approaches (including LLMs and agentic AI) to catch more while reviewers do less. Own the platform that serves those models in production, from APIs and SDKs to feature stores and the integration points that make onboarding new surfaces turnkey. Keep it fast, reliable, and scalable as usage grows. Partner with fraud investigators, Trust & Safety, and platform teams to turn new fraud patterns into new defenses, fast. What you need to succeed: Bachelor's or Master's in Computer Science, Statistics, ML, or related — or equivalent experience. Deep experience with ML techniques — supervised/unsupervised learning, anomaly detection, ensembles — applied to fraud or risk detection. 5+ years building production ML systems and scalable backend services. Comfortable owning a model's full lifec