4 hours ago
London, United KingdomSenior
Responsibilities
- Design and build a declarative, standardized training pipeline for data scientists.
- Build model packaging and serving abstractions supporting classical ML, deep learning, and emerging model architectures.
- Implement model evaluation with standardized metrics, reproducible comparisons, and automated validation gates.
- Build model monitoring for drift detection, performance alerts, automated retraining triggers, and regulatory audit trails.
- Own the integration layer between FinCrime lifecycle tooling and Wise’s central ML infrastructure.
- Improve data science productivity by replacing manual ML operations with scalable internal platform capabilities.
Requirements
- Production experience building ML platform infrastructure such as training pipelines, model serving, evaluation frameworks, or monitoring systems.
- Strong software engineering fundamentals and experience with Python, Kotlin or Java, and SQL.
- Experience with ML orchestration tools such as Airflow or Kubeflow, model registries such as MLflow, and container-based deployment.
- End-to-end understanding of the ML lifecycle from data ingestion through training, packaging, serving, and monitoring.
- A product mindset for internal tooling and experience building platforms adopted by other teams.
Benefits
- Starting salary of £87,500 - £111,000 plus RSUs.
- Hybrid work arrangement.
- Local candidates are supported with transfer of visa sponsorship; relocation support is not provided.
About Wise
Wise builds a cross-border payments and multi-currency account used by individuals and businesses to send, receive, hold, and spend money in 40+ currencies. Revenue comes from transfer and account fees, card interchange, and enterprise partnerships via Wise Platform, which lets banks and large companies embed its international payments. Founded in 2011 as TransferWise, Wise is a London-headquartered public company.