Senior AI / Machine Learning Engineer — Fraud Prevention

Written by the T&S Network from Adobe's posting.

Builds ML systems for fraud and abuse detection, covering anomaly detection, user and device risk analysis, identity and service abuse, and new AI abuse scenarios. The role owns detection from signals and modeling through production deployment and real-time decisioning, and works across engineering, product, and risk teams to ship it.

• Fraud and scams
• AI misuse and jailbreaks

What you'd do

• Train and ship models that flag fraud, abuse, anomalies, and assign risk scores.
• Build risk features from account, device, network, behavior, velocity, and session data.
• Wire ML and AI outputs into live risk decisions and automated enforcement.
• Use LLMs and AI agents to widen detection, investigation, and classification coverage.
• Turn new attack patterns and research into models, signals, and mitigations.
• Assess solutions on accuracy, latency, cost, and effect on customers.
• Monitor models and watch for drift as attacker behavior changes.

What it asks for

• Solid ML background with practical Python, SQL, and PyTorch experience.
• Experience taking ML systems from feature engineering through production deployment and monitoring.
• Strong engineering skills across ML, backend services, and data infrastructure.
• Experience building with LLMs or AI agents, especially for AI and generation abuse use cases.

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