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.
More about our mission and what we offer.
Job DescriptionAbout the role:
- Wise is one of the fastest-growing global financial platforms, and as we scale, so does the sophistication of the ML systems protecting every transaction. Our Risk ML team is building the model lifecycle platform that makes it possible to develop, deploy, and monitor ML models for financial crime detection - reliably, reproducibly, and at scale.
- We're looking for a Senior ML Platform Engineer to build this platform from the ground up. You'll design the infrastructure that turns model development from a bespoke, manual process into a scalable, standardised one - so our data and applied scientists can focus on improving detection rather than managing operations.
This is a greenfield build with strong investment and direct engagement from Wise's senior leadership.
How we work:
- Risk ML sits within Wise’s FinCrime organisation, owning the full ML and AI foundation for financial crime detection. We're scaling into three dedicated pillars - Feature Platform, Learning Loop, and Risk Modelling. You'll sit in Risk Modelling, building the platform layer that makes scaling our detection capabilities possible.
- You’ll work closely with data scientists, feature platform engineers (upstream infrastructure), and Wise's central ML platform team (shared foundations). We value engineers who build for adoption - internal platforms succeed when teams want to use them.
What will you be working on?
- Designing and building the declarative training pipeline - standardised, config-driven model training that any data scientist can use without writing deployment code
- Building model packaging and serving abstraction - a unified interface that handles multiple model types (classical ML, deep learning, emerging architectures) through a consistent API
- Implementing the model evaluation framework - standardised metrics, reproducible comparison, and automated validation gates
- Building model monitoring - drift detection, performance degradation alerts, automated retraining triggers, and full audit trails for regulatory compliance
- Owning the integration layer with Wise's central ML infrastructure - aligning on boundaries so FinCrime-specific lifecycle tooling builds cleanly on shared foundations
- Maximising data science productivity - your platform's success is measured by how much time shifts from operational maintenance to improving detection performance
What do you need?
- Experience building ML platform infrastructure in production - training pipelines, model serving, evaluation frameworks, or monitoring systems. Infrastructure that other teams depend on, not individual model work.
- Strong software engineering fundamentals - you build reliable, well-tested, maintainable systems. Python, Kotlin/Java, SQL.
- Experience with ML orchestration (Airflow, Kubeflow, or equivalent), model registries (MLflow or similar), and container-based deployment
- End-to-end understanding of the ML lifecycle - data ingestion through training, packaging, serving, and monitoring - and knowledge of where things break
- A product mindset for internal tooling - you think about data scientists as users and build for adoption, not just functionality
Nice to Have:
- Model serving at scale - latency optimisation, ONNX packaging, canary deployments for models
- Experience in FinCrime, fraud, AML, or regulated environments where audit trails and model governance are non-negotiable
- Experience with model monitoring and drift detection systems in production
- Track record of migrating teams from manual ML workflows to platform-based approaches
Interested? Find out more:
How we work – a practical guide
DEI @ Wise
Wise Tech Stack (2025 update)
See what it's like to work at Wise London!
Our Engineering career map
Wise Engineering – https://medium.com/wise-engineering
What do we offer:
Starting salary: £111,000 - £145,000 + RSUs
Wise Benefits
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Additional InformationFor 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 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 it's like to work at Wise visit Wise.Jobs.
Keep up to date with life at Wise by following us on LinkedIn and Instagram.
Skills Required
- Experience building ML platform infrastructure in production (training pipelines, model serving, evaluation, monitoring).
- Strong software engineering fundamentals; experience building reliable, well-tested, maintainable systems.
- Python, Kotlin/Java, and SQL proficiency.
- Experience with ML orchestration (Airflow, Kubeflow, or equivalent).
- Experience with model registries (MLflow or similar).
- Experience with container-based deployment.
- End-to-end understanding of the ML lifecycle (data ingestion through training, packaging, serving, monitoring).
- Product mindset for internal tooling and user-focused platform design.
- Model serving at scale (latency optimisation, ONNX packaging, canary deployments).
- Experience in FinCrime, fraud, AML, or regulated environments with audit and governance requirements.
- Experience with model monitoring and drift detection systems in production.
- Track record migrating teams from manual ML workflows to platform-based approaches.
What the Team is Saying
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Wise Compensation & Benefits Highlights
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Equity Value & Accessibility — Equity is granted to all employees via time‑based RSUs/stock awards, aligning staff with company performance. This broad accessibility makes ownership a core part of total rewards.
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Leave & Time Off Breadth — Policies include a global minimum of 33–36 paid days off and a paid six‑week sabbatical after four years with a cash stipend. The sabbatical is positioned as a standard milestone benefit in addition to annual leave.
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Parental & Family Support — Wise commits to a minimum of 18 weeks’ fully paid parental leave for birth or adoption across many offices. Eligibility rules and tenure may apply by location while maintaining a companywide minimum standard.
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What We Do
Wise is a global technology company, building the best way to move and manage the world's money. With Wise Account and Wise Business, people and businesses can hold 40 currencies, move money between countries and spend money abroad. Large companies and banks use Wise technology too; an entirely new network for the world's money. Launched in 2011, Wise is one of the world’s fastest growing, profitable tech companies. In fiscal year 2025, Wise supported around 15.6 million people and businesses, processing over $185 billion in cross-border transactions and saving customers around $2.6 billion.
Why Work With Us
We’re truly global in who we are, how we work, and how we build. Everything we do is centred around creating a world of money that’s fast, easy, fair. And open to all. Everyone who works here owns a piece of Wise, from the work they do, to the stock they hold.
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Employees engage in a combination of remote and on-site work.
We expect new joiners in the office most days to build connections and learn from colleagues for their first six months. After that, most Wisers split their working week between the office and home, typically coming in at least 12 times a month.
