Senior ML Platform Engineer II - Financial Crime
For everyone, everywhere. We're people building money without borders.
You'll build ML platform infrastructure using Python, Kotlin/Java, SQL, Airflow, Kubeflow, MLflow, and container-based deployment. Your team owns the model lifecycle platform for financial crime detection – designing training pipelines, model serving, evaluation, and monitoring systems from the ground up. This is a greenfield build with strong senior leadership investment.
Free Tailor for ATS: 10/10 runs left
Wise is a global technology company, building the best way to move and manage the world’s money.
Min fees. Max ease. Full speed.
Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money.
As part of our team, you will be helping us create an entirely new network for the world's money.
For everyone, everywhere.
More about our mission and what we offer.
About the role:
Wise is one of the fastest-growing global financial platforms, and as we scale, so does the sophistication of the ML systems protecting every transaction. Our Risk ML team is building the model lifecycle platform that makes it possible to develop, deploy, and monitor ML models for financial crime detection - reliably, reproducibly, and at scale.
We're looking for a Senior ML Platform Engineer to build this platform from the ground up. You'll design the infrastructure that turns model development from a bespoke, manual process into a scalable, standardised one - so our data and applied scientists can focus on improving detection rather than managing operations.
This is a greenfield build with strong investment and direct engagement from Wise's 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're scaling into three dedicated pillars - Feature Platform, Learning Loop, and Risk Modelling. You'll sit in Risk Modelling, building the platform layer that makes scaling our detection capabilities possible.
You’ll work closely with data scientists, feature platform engineers (upstream infrastructure), and Wise's central ML platform team (shared foundations). We value engineers who build for adoption - internal platforms succeed when teams want to use them.
What will you be working on?
Designing and building the declarative training pipeline - standardised, config-driven model training that any data scientist can use without writing deployment code
Building model packaging and serving abstraction - a unified interface that handles multiple model types (classical ML, deep learning, emerging architectures) through a consistent API
Implementing the model evaluation framework - standardised metrics, reproducible comparison, and automated validation gates
Building model monitoring - drift detection, performance degradation alerts, automated retraining triggers, and full audit trails for regulatory compliance
Owning the integration layer with Wise's central ML infrastructure - aligning on boundaries so FinCrime-specific lifecycle tooling builds cleanly on shared foundations
Maximising data science productivity - your platform's success is measured by how much time shifts from operational maintenance to improving detection performance
What do you need?
Experience building ML platform infrastructure in production - training pipelines, model serving, evaluation frameworks, or monitoring systems. Infrastructure that other teams depend on, not individual model work.
Strong software engineering fundamentals - you build reliable, well-tested, maintainable systems. Python, Kotlin/Java, SQL.
Experience with ML orchestration (Airflow, Kubeflow, or equivalent), model registries (MLflow or similar), and container-based deployment
End-to-end understanding of the ML lifecycle - data ingestion through training, packaging, serving, and monitoring - and knowledge of where things break
A product mindset for internal tooling - you think about data scientists as users and build for adoption, not just functionality
Nice to Have:
Model serving at scale - latency optimisation, ONNX packaging, canary deployments for models
Experience in FinCrime, fraud, AML, or regulated environments where audit trails and model governance are non-negotiable
Experience with model monitoring and drift detection systems in production
Track record of migrating teams from manual ML workflows to platform-based approaches
Interested? Find out more:
How we work – a practical guide
DEI @ Wise
Wise Tech Stack (2025 update)
See what it's like to work at Wise London!
Our Engineering career map
Wise Engineering – https://medium.com/wise-engineering
What do we offer:
Starting salary: £111,000 - £145,000 + RSUs
Wise Benefits
#LI-AB3 #LI-Hybrid
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.