Description
Wise is a global technology company, building the best way to move and manage the world's money. Min fees. Max ease. Full speed.
As part of our team, you will be helping us create an entirely new network for the world's money. For everyone, everywhere.
The Machine Learning Platform (MLP) team builds and maintains the infrastructure that enables data scientists across Wise to develop, deploy, serve, and monitor machine learning models at scale. Your mission and role will be building and maintaining a cost-efficient and scalable machine learning platform.
Your input will directly affect how Wise is making decisions and predictions on billions of events.
We are looking for a Senior Software Engineer to join our team and help us evolve from a collection of tools into a coherent, self-service platform.
Responsibilities
- Build and maintain core ML platform services including model serving infrastructure, training pipelines, and experiment tracking
- Contribute to the evolution of our platform from individual service offerings towards a coherent, user-driven product
- Improve platform scalability, reliability, and operability, ensuring our infrastructure can support hundreds of models in production
- Improve observability and monitoring across the model lifecycle, helping data scientists understand model health and performance
- Collaborate with data scientists to understand their workflows, pain points, and needs
- Participate in on-call/support rotation, contributing to platform stability and identifying opportunities to reduce operational toil
- Help shape the technical and product roadmap by contributing to discovery, spikes, and architectural decisions
- Share knowledge across the team, reduce silos, mentor others, and help raise engineering standards
Requirements
- Strong engineering background in mainstream languages like Java or Python (preferably Python) with experience building and maintaining production systems
- Experience with platform or infrastructure engineering, including building or contributing to internal developer platforms or self-service tooling
- Experience with Kubernetes - deploying, managing, and troubleshooting containerised workloads
- Experience with cloud infrastructure (ideally AWS) including compute, storage, and networking
- Strong understanding of distributed systems principles
- Solid understanding of software engineering best practices - testing, code review, CI/CD, and clean, maintainable code
Nice to haves
- Familiarity with ML platform tooling such as MLflow, Airflow, or similar orchestration and experiment tracking frameworks
- Familiarity with ML systems and workflows - training, serving, feature engineering, model monitoring
- Experience with Infrastructure as Code (Terraform, CDK, or similar)
- Exposure to streaming or batch data processing frameworks (Spark, Flink, Kafka)
What you get back
- The opportunity to shape a platform that directly enables ML-driven decisions across a global financial product serving millions of customers
- A team that values autonomy, experimentation, and continuous improvement
- Real ownership of the systems you work on - from architecture decisions to production operations
- Exposure to complex, real-world ML infrastructure challenges at scale