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 protects millions of customers and billions in transactions from fraud, money laundering and financial crime. Our ML systems are the front line of defense - operating at a global scale of 100K requests/minute under strict sub-50ms latency SLAs. We need an exceptional technical leader to own how these models are engineered, shipped and scaled.
We're hiring a Senior ML Engineering Lead to build and grow Wise's Risk Modelling engineering pillar. You will own the full model lifecycle standard for financial crime detection - from offline experimentation to production deployment and real-time monitoring and build the team to execute it. Your job is to build the automated engineering ecosystem and organisation that scales this safely to hundreds of models.
This is a rare greenfield leadership role with strong investment and engagement from Wise's CTO and 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 have three dedicated pillars - Feature Platform, Learning Loop and Risk Modelling. You'll lead the Risk Modelling pillar, leading a team of Senior ML Systems Engineers and Applied ML Engineers.
We operate with high autonomy and low hierarchy. You'll own the engineering strategy end-to-end - from architecture decisions and infrastructure design through to hiring, team culture and cross-platform partnerships. We value leaders who shape direction and build teams, not just manage delivery.
What will you be working on?:
The Model Factory: Architect the declarative pipeline that turns a configuration file into a deployed, monitored model - the engineering backbone for scaling to hundreds of models
The Experimentation Engine: Establish the reusable path from research (partnering with DS Research) to high-throughput production for traditional and modern architectures
Model Operations: Build the infrastructure for automated retraining, drift detection, threshold simulation/management and audit trails - the operational layer required to run hundreds of models safely at scale
The Team: Recruit, lead and mentor a world-class team of ML engineers. Establish a high-performance, engineering-first culture from scratch - setting hiring standards, technical bar and growth paths
Cross-Platform Partnership: Define and navigate the partnership with key platform teams - owning the build vs consume decisions for your pillar
What do you need?:
You've explicitly led or built an ML Engineering or model lifecycle automation team (not just used one) at a high-growth company - you defined the standards that other engineering teams followed
System-level and mathematical depth: you can design a model factory architecture, review a training pipeline & debug a runtime inference latency regression
Experience in high-throughput environments where latency constraints are tight and model failures carry massive financial consequences
Track record of hiring and developing senior engineers - you've built a team, not just inherited one
Ability to navigate ambiguity and make architecture-level decisions with incomplete information - this is a greenfield build, not an optimisation role
Strong enough technically to guide and review across deep learning, ML systems and production infrastructure - you lead through depth, not just delegation
Nice to Have:
Experience at a tier-1 fintech or payments company
Experience with graph-based methods (GNNs, entity resolution) in production
Foundation model fine-tuning or LLM evaluation experience
Experience establishing ML engineering practices in organisations transitioning from classical ML to deep learning
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: £135,000 - £175,000 + RSUs
Wise Benefits
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Additional Information
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 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
- Led or built an ML Engineering or model lifecycle automation team at a high-growth company
- System-level and mathematical depth to design model factory architecture and debug training and inference issues
- Experience in high-throughput, low-latency environments where model failures have large financial impact
- Proven track record of hiring and developing senior engineers
- Ability to make architecture-level decisions with incomplete information
- Strong technical ability across deep learning, ML systems, and production infrastructure
- Experience at a tier-1 fintech or payments company
- Experience with graph-based methods (GNNs, entity resolution) in production
- Foundation model fine-tuning or LLM evaluation experience
- Experience establishing ML engineering practices transitioning from classical ML to deep learning
What the Team is Saying
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Wise Compensation & Benefits Highlights
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Leave & Time Off Breadth — Global paid time off is presented as 33–36 days including local public holidays, plus three “Me Days,” with a paid six‑week sabbatical and stipend after four years. Work-from-anywhere for up to 90 days per year after six months further complements time away and flexibility.
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Equity Value & Accessibility — RSUs are granted to all employees, enabling broad ownership in the company. Equity is positioned as a standard, company‑wide component of total rewards.
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Parental & Family Support — Parental leave is described as generous across markets, with U.S. pages listing up to 18 weeks fully paid for birthing parents and 8 weeks for non‑birthing parents. Additional family‑oriented supports such as onsite Mother’s Rooms and abortion travel benefits are noted in certain locations.
Wise Insights
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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Wise Offices
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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.
