Senior ML Engineering Lead - Financial Crime

This job is with Wise, an inclusive employer and a global technology company, building the best way to move and manage the world’s money. Wise is on a mission to make lives easier and save money for people and businesses sending money internationally.

About the role: Wise protects millions of customers and billions in transactions from fraud, money laundering and financial crime. Our ML systems are the front line of defense, operating at a global scale of 100K requests/minute under strict sub-50ms latency SLAs. We need an exceptional technical leader to own how these models are engineered, shipped and scaled. We're hiring a Senior ML Engineering Lead to build and grow Wise's Risk Modelling engineering pillar. You will own the full model lifecycle standard for financial crime detection - from offline experimentation to production deployment and real-time monitoring and build the team to execute it. Your job is to build the automated engineering ecosystem and organisation that scales this safely to hundreds of models.

This is a rare greenfield leadership role with strong investment and engagement from Wise's CTO and senior leadership.

How we work: Risk ML sits within Wise's FinCrime organisation, owning the full ML and AI foundation for financial crime detection. We have three dedicated pillars - Feature Platform, Learning Loop and Risk Modelling. You'll lead the Risk Modelling pillar, leading a team of Senior ML Systems Engineers and Applied ML Engineers. We operate with high autonomy and low hierarchy. You'll own the engineering strategy end-to-end - from architecture decisions and infrastructure design through to hiring, team culture and cross-platform partnerships. We value leaders who shape direction and build teams, not just manage delivery.

What will you be working on?:

• The Model Factory: Architect the declarative pipeline that turns a configuration file into a deployed, monitored model - the engineering backbone for scaling to hundreds of models.
• The Experimentation Engine: Establish the reusable path from research (partnering with DS Research) to high-throughput production for traditional and modern architectures.
• Model Operations: Build the infrastructure for automated retraining, drift detection, threshold simulation/management and audit trails - the operational layer required to run hundreds of models safely at scale.
• The Team: Recruit, lead and mentor a world-class team of ML engineers. Establish a high-performance, engineering-first culture from scratch - setting hiring standards, technical bar and growth paths.
• Cross-Platform Partnership: Define and navigate the partnership with key platform teams - owning the build vs consume decisions for your pillar.

What do you need?: You've explicitly led or built an ML Engineering or model lifecycle automation team at a high-growth company - you defined the standards that other engineering teams followed. System-level and mathematical depth: you can design a model factory architecture, review a training pipeline.