About the Role
Wise is one of the fastest-growing global financial platforms, and as it scales, so does the sophistication of the ML systems protecting every transaction. The 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.
Responsibilities
•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
Requirements
•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
Compensation
Starting salary: £87,500 - £111,000 + RSUs
