Senior Software Engineer I Machine Learning Platform

About the role

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 reduce silos mentor others and help 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 dont 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

Interested Find out more:

• How we work a practical guide

• DEI @ Wise

• Wise Tech Stack (2025 update)

• See what its like to work at Wise London!

What do we offer: 

• Starting salary: 87500 - 111000   RSUs

• Wise Benefits

• #LI-AB3 #LI-Hybrid

• Our Engineering career map

• Wise Engineering Information :

  For everyone everywhere. Were people building money without borders  without judgement or prejudice too. We believe teams are strongest when they are diverse equitable and inclusive.

  Were proud to have a truly international team and we celebrate our differences.
  Inclusive teams help us live our values and make sure every Wiser feels respected empowered to contribute towards our mission and able to progress in their careers.

  If you want to find out more about what its like to work at Wise visit .

  Keep up to date with life at Wise by following us on LinkedIn and Instagram.

  
  

  Remote Work :

  No

  
  

  Employment Type :

  Full-time