Senior Software Engineer I - Machine Learning Platform

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 at scale. Within 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 at scale. Our 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 infrastructure and orchestration to APIs and developer tooling
• You take ownership of problems end-to-end, from understanding the need through to production and beyond
• You communicate clearly, build consensus, and enjoy collaborating with people from different disciplines - data scientists, product managers, and fellow engineers
• You have a growth mindset - curious, experimental, and open to giving and receiving regular feedback
• You share your ideas, continuously improve yourself and the team around you, and are comfortable working collaboratively in a hybrid environment

What do you need?

We are fully aware that it is uncommon for a candidate to have all skills required and we fully support everyone in learning new skills with us. We value potential and enthusiasm as much as existing expertise. So if you have some of those listed below and are eager to learn more we do want to hear from you!

• Strong engineering background in Python with experience building and maintaining production systems
• Experience with Kubernetes - deploying, managing, and troubleshooting containerised workloads
• Familiarity with ML platform tooling such as MLflow, Airflow, or similar orchestration and experiment tracking frameworks
• Experience with cloud infrastructure (AWS or GCP) including compute, storage, and networking
• Understanding of distributed systems principles - you know the trade-offs between different architectures and can make pragmatic decisions
• Experience with observability and monitoring - building dashboards, alerts, and tooling that helps teams understand system health
• Solid understanding of software engineering best practices - testing, code review, CI/CD, and clean, maintainable code
• Ability to use AI-assisted development tools responsibly, while validating outputs and retaining ownership of code quality

Nice to haves

• Experience building or contributing to internal developer platforms or self-service tooling
• Familiarity with ML workflows - training, serving, feature engineering, model monitoring (you don’t need to be a data scientist, but understanding the domain helps)
• Experience with Infrastructure as Code (Terraform, CDK, or similar)
• Exposure to streaming or batch data processing frameworks (Spark, Flink, Kafka)
• Interest in platform-as-product thinking - treating adoption, user experience, and feedback loops as first-class concerns

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 - where your ideas about how we work matter as much as what we build
• Real ownership of the systems you work on - from architecture decisions to production operations
• Exposure to complex, real-world ML infrastructure challenges at scale
• A collaborative environment where people are grounded, driven, and genuinely enjoy working with others

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 Wise employee 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.