About the job
We are seeking a Senior Staff Engineer to define the architectural direction for risk mitigation across Adobe. Lead the development of next-generation risk platforms, drive AI and ML integration, and mentor technical teams. Ideal for experienced architects with deep expertise in high-scale platforms and distributed systems.
Responsibilities
Architect systems built for the next three to five years of risk mitigation, while delivering impact iteratively and solving today's most important priorities along the way.
Push the platform to the forefront of AI and ML to strengthen fraud and abuse detection.
Lay the foundational building blocks of the Risk Platform that future risk-mitigation capability builds on, including the platform's shift toward event-driven, continuously-reasoning risk intelligence.
Set architectural standards for risk-decisioning services that other teams across Adobe adopt.
Share architectural thinking broadly, through design docs that become org-wide standards, white papers, tech talks, or cross-org design reviews.
Simplify complex, interlinked systems into designs other teams can build on with confidence.
Mentor senior and staff engineers across teams, growing the next generation of technical leaders.
Qualifications
Minimum
Bachelor's or advanced degree in Computer Science or related field, or equivalent experience; Master's preferred.
12+ years architecting high-scale, highly performant platforms, and leading the technical teams that build them.
Deep understanding of AI and ML, both to strengthen fraud detection and to counter AI-powered attacks.
Deep expertise in distributed systems and databases, including SQL, NoSQL, and DynamoDB-class stores.
Track record architecting systems with multi-year relevancy, not just solving today's scaling or reliability problem.
Experience with data lakes and large-scale data platforms like Databricks or Spark.
Proven ability to influence engineering direction across teams and Business Units without formal authority.
A track record of publishing or presenting technical work that shaped how others build.
Preferred
Experience with graph databases or identity/relationship graphs applied to fraud or abuse detection (e.g., detecting rings, relay networks, or coordinated account abuse via graph traversal).
Device fingerprinting or AI agent design experience.
A payments, commerce, or fraud background.
Familiarity with regulatory and compliance constraints relevant to payments and fraud decisioning (e.g., PCI, regional payment regulations).