The opportunity
Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At Adyen, everything we do is engineered for ambition.
What you'll do
Technical Strategic & Architecture: Own the multi-year technical north star vision for the group, together with the technical leads of each team in the group. Design and evolve high-throughput, low-latency distributed systems capable of processing real-time merchant transactions, integrating complex machine learning models, and handling massive big data pipelines.
Leadership Partnership: Partner closely with the Director of Engineering to assess organizational health, surface systemic engineering bottlenecks, and align long-term technical investments with Merchant protection goals.
Product Strategy: Shape the strategic roadmap alongside the Director of Engineering, fellow Staff Engineers, and Product leadership.
Cross-Team Alignment: Establish consistent architectural patterns, engineering practices, and quality bars across 3 fraud-focused teams. Bring clarity to ambiguity as new fraud vectors and product requirements emerge.
Talent Multiplier & Mentorship: Sponsor and mentor senior engineers, build clear growth paths, model high engineering standards, and foster a culture of engineering excellence and psychological safety.
ML & Data Infrastructure Evolution: Partner with Data Science and ML Engineering so infrastructure supports fast model deployment, feature stores, and real-time inference, without compromising latency or reliability.
What they're looking for
- Fraud Ops Collaboration: Work closely with Fraud Operations to translate emerging fraud patterns and investigative findings into platform and tooling requirements. Ensure engineering systems give Fraud Ops the visibility, control, and response speed they need to act on evolving threats
- Technical leader: A track record of setting technical strategy through architectural judgment and clear communication via influence, not organizational authority.
- Distributed Systems Expertise: Deep experience designing and operating large-scale, production-grade distributed systems with a focus on reliability, performance, and resilience.
- ML Systems Experience: Comfortable architecting the infrastructure layer around ML workloads (e.g., feature stores, real-time inference pipelines, model serving) even without being an ML practitioner yourself; strong appetite for close, ongoing collaboration with Data Science/ML Engineering.
