Senior Software Engineer - Machine Learning Platform

Senior Software Engineer I - Machine Learning Platform

• Full-time
• Compensation: GBP 87,500 - GBP 111,000 - yearly

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.

For our customers, Wise should feel as simple as sending money from A to

B. Behind that simplicity is a complex engine of currencies, routes, products, and features, generating terabytes of data every day.

Data Products & Insights helps Wise turn that data into products, insights, and decisions hin this area, 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  platform powers predictions and decisions across the business - from fraud detection to treasury management to product personalisation - directly impacting how Wise serves millions of customers worldwide.

Your mission and role will be building and maintaining a cost efficient and scalable machine learning platform, that is a delight to use and that provides a good engineering and data science experience while shortening the full experimentation feedback loop - a data scientist does not just deploy models fast, but learns fast which model is better. 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 in London and help us evolve from a collection of tools into a coherent, self-service platform.

How we work:

We are a small, collaborative team that values product thinking, shared ownership, and continuous improvement. We are in the early stages of introducing structured agile practices and treat every process change as an experiment.

The MLP team is part of the Data Products & Insights Squad. We own the infrastructure layer that sits between data scientists and production: model serving, training pipelines, model registry and experiment tracking, feature management, and model monitoring on the line. Our customers are internal - Data Scientists and ML engineers across Wise - and our success is measured by how effectively they can build, deploy, and iterate on models without friction.

What will you be working on?

• Building and maintaining core ML platform services including model serving infrastructure, training pipelines, and experiment tracking
• Contributing to the evolution of our platform from individual service offerings towards a coherent, user-driven product
• Improving platform scalability, reliability, and operability, ensuring our infrastructure can support hundreds of models in production while making pragmatic trade-offs around cost, complexity, and user needs
• Improving observability and monitoring across the model lifecycle, helping data scientists understand model health and performance
• Collaborating with data scientists to understand their workflows, pain points, and needs - treating them as your customers
• Participating in on-call/support rotation, contributing to platform stability and identifying opportunities to reduce operational toil
• Helping shape the technical and product roadmap by contributing to discovery, spikes (exploratory/investigative work), and architectural decisions
• Sharing knowledge across the team, reducing silos, mentoring others, and helping raise engineering standards through design reviews, code reviews, documentation, and continuous improvement

What does it take?

• You care about bringing value and satisfaction to your customers - the developer/user experience of the people who use your platform matters as much as the technical elegance of the solution
• You think in systems, not just features - you consider how components interact, where complexity lives, and how to reduce it
• You are comfortable working across the stack - from…