Senior Software Engineer II - Fraud Squad - Account Integrity (Java)

Join us as a Software Engineer in our critical Scam Prevention team. This is a high-impact engineering role with a strong product focus, dedicated to solving complex, real-world adversarial problems to directly protect our global customer base., This defining role involves continuously improving our financial crime prevention infrastructure and systems. You will deliver a direct, measurable impact on customer security by taking end-to-end ownership of projects focused on:

• Building and refining critical controls to prevent Wise customers from scams.
• Drive Backend and Frontend for our scam prevention and detection systems - You will be working 80% of the time on a backend and 20% on the frontend
• You will be delivering a tangible, real-world impact by securing customers’ money, accelerating product growth by raising automation levels, and helping us engineer a world-class money transfer product.

• Engineering Expertise: Professional experience with Java or other JVM-based languages, including the Spring framework. Experience building and designing scalable web applications that handle large amounts of traffic - ideally using React based frontend applications, Javascript / Typescript.
• System Design & Operation: Proven experience designing and operating CI/CD pipelines, Distributed, and Concurrent Systems.
• Product Ownership: A strong product mindset, accountability, and the ability to drive projects independently, making data-driven decisions with the customer in mind.
• Data Proficiency: Experience with relational and non-relational databases, including query optimisation and designing resilient schemas.
• Code Quality: Exceptional fundamentals, sharp problem-solving ability, and a commitment to Test Driven Development and best coding practices.
• Collaboration: Excellent communication skills to articulate complex technical concepts across teams (Product, Data Science, Investigations).

Nice to Have:

• Familiarity with the machine learning lifecycle (data pipelines, feature engineering, and deploying ML systems into production).