Senior Software Engineer - Risk Intelligence Platform

Job Description

Wise moves billions every year for millions of people and businesses across multiple countries and currencies. At that scale, financial crime is constantly evolving — from scams and account takeovers to money laundering and mule networks. Staying ahead of these threats means our automated controls and machine learning models need high-quality, timely insights.

The Risk Intelligence Platform team, part of Risk Machine Learning, builds the tools and automation that help Wise's FinCrime teams develop and improve those models. Our work covers areas such as model experimentation, bias and fairness, automated training-set maintenance, and threshold analysis.

Our main users are Data Scientists and Product Managers across Fraud Prevention, AML and Sanctions. We build the platform and tooling that removes repetitive Engineering work and enables these teams to focus on what matters most: identifying and preventing financial crime effectively at scale.

As part of the team, you'll work at the intersection of Backend Engineering, data and machine learning, solving complex Engineering problems that directly support Wise's FinCrime prevention capabilities.

Qualifications

As a Senior Backend Engineer, your job will be to provide guidance for Wise's data engineering and machine learning community, taking ownership of difficult technical problems. 

Minimum requirements:

• Typically 7+ years of experience with Java (11+, 21+), although we care more about the depth and relevance of your experience than the number of years.
• Familiarity with Python 
• Experience with batch or stream processing (preferably Apache Spark and Flink/Kafka streams) 
• Familiarity with SQL and no-SQL (document) databases
• Experience with complex systems distributed across several microservices, Kafka, scattered data and ownership
• Hands-on knowledge of system integration patterns
• A strong product mindset and passion for user experience, you prioritise work with the customers in mind and drive data-driven decisions to fix customer pain-points
• Strong problem-solving skills to help refine problem statements and figure out how to solve them with the available data and from first principles
• High level of ownership, including identifying impactful problems to solve, getting buy-in from stakeholders and implementing the solution end-to-end

Nice to have:

• ML Engineering background, such as model training pipelines, automated label quality assurance, model experimentation frameworks
• Data Engineering background, such as experience with large-scale data pipelines, feature engineering and serving infrastructure, familiarity with graph algorithms and/or embedding techniques, awareness of data governance practices
• Experience with Apache Iceberg and/or Trino

What does success look like in this role?

• You’ll have onboarded and found your place through understanding your team and tribe vision and how you can contribute
• You’ll understand how our values can help you guide your work
• You’ll understand the reasons behind problematic payments and customer difficulties and how to go about solving them
• Understand our customers and the impact our product makes in their lives 
• You'll be having a real world impact by building financial crime prevention systems which protect customers
• You’ll help us scale-up and build a world class money transfer product by finding solutions to our technical challenges and opportunities

Additional Information

For everyone, everywhere. We're people building money without borders  — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.

We're proud to have a truly international team, and we celebrate our differences.
Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.

If you want to find out more about what it's like to work at Wise visit Wise.Jobs.

Keep up to date with life at Wise by following us on LinkedIn and Instagram.

Compensation: 2250000 to 2700000 (HUF) (MONTHLY)