Staff Java Engineer - Merchant Fraud Prevention

Slashhash; Amsterdam; Hybrid; EUR 140,000 - 190,000; Full time

Job summary

Adyen in Amsterdam is seeking a Staff Java Engineer for the Merchant Fraud Prevention group. You will define the long-term technical vision across 2-3 engineering teams, architect high-throughput distributed systems, and partner with Data Science, ML Engineering, and Fraud Operations.

You will lead the technical north star, design real-time transaction processing, integrate ML models, and raise engineering standards while mentoring senior engineers and shaping the roadmap.

Qualifications

• Must-have deep distributed systems expertise.
• Experience building ML-enabled systems.
• Strong collaboration and leadership abilities.

Responsibilities

• Own the multi-year technical north star vision across 2–3 teams.
• Design high-throughput, low-latency distributed systems for real-time merchant data.
• Integrate complex ML models with streaming data pipelines.
• Partner with Engineering Director to align long-term investments with fraud goals.
• Establish architectural patterns and quality standards across fraud-focused teams.
• Mentor senior engineers and foster engineering excellence and safety.
• Collaborate with Data Science/ML engineering to support fast model deployment and feature stores.
• Translate fraud patterns into platform and tooling requirements.

Skills

Java

Distributed Systems

Machine Learning

Feature Stores

Real-time Inference

Model Serving

Big Data Pipelines

Security-by-design

Regulated Data Handling

Job description

Adyen is seeking a Staff Java Engineer for its Merchant Fraud Prevention group in Amsterdam. The role involves defining the long-term technical vision across 2-3 engineering teams, architecting high-throughput distributed systems, and partnering with Data Science, ML Engineering, and Fraud Operations. Candidates need deep distributed systems expertise, ML systems experience, and a track record of technical leadership.

Technical (Must-have)

• Java
• Distributed Systems
• Machine Learning
• Feature Stores
• Real-time Inference
• Model Serving
• Big Data Pipelines
• Security-by-design
• Regulated Data Handling

Soft Skills

• Technical Leadership
• Communication
• Mentorship
• Relationship Building
• Proactive
• Collaboration
• Clarity through ambiguity

Technical (Nice-to-have)

• LLMs
• RAG
• Evals
• Policy Enforcement

Key Responsibilities

• 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
• 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
• Shape the strategic roadmap alongside the Director of Engineering, fellow Staff Engineers, and Product leadership
• 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
• Sponsor and mentor senior engineers, build clear growth paths, model high engineering standards, and foster a culture of engineering excellence and psychological safety
• Partner with Data Science and ML Engineering so infrastructure supports fast model deployment, feature stores, and real-time inference, without compromising latency or reliability
• 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

Java, Distributed Systems, Machine Learning, Feature Stores, Real-time Inference, Model Serving, Big Data Pipelines, Security-by-design, Regulated Data Handling