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 moves billions across borders every year. Behind every transaction is a decision: is this safe? Our ML systems make that call - at scale, in real time, across every market we operate in.
Our Risk ML team is building the next generation of financial crime detection at Wise - investing in modern architectures like deep learning, graph neural networks, and foundation models to detect increasingly sophisticated fraud and money laundering patterns. We're looking for a Staff Applied ML Engineer to lead this evolution: defining the architecture strategy, shipping production neural models, and building the blueprint that scales across FinCrime domains.
This is a greenfield opportunity - you'll be setting the direction for how Wise applies modern ML to financial crime risk, with strong investment and engagement from 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, working alongside data scientists, platform engineers, product and domain experts.
We operate with high autonomy and low hierarchy. You'll own problems end-to-end - from research and architecture decisions through to production deployment and impact measurement. We value engineers who shape direction, not just execute tickets.
What will you be working on?
- Designing and shipping ML and deep learning models for financial crime detection - sequence-based, graph-based, attention-based - serving real-time decisions at Wise's scale
- Defining the architecture strategy for how Wise applies modern ML to risk - which model families, which serving patterns, which training paradigms
- Building the reusable end-to-end pipeline pattern - from experimentation through training to production deployment - that future models follow
- Evaluating and prototyping foundation model and embedding approaches for transaction representation across FinCrime domains
- Partnering with Data Science on model evaluation, experimentation design and causal measurement in domains where clean A/B testing isn't always possible
- Mentoring engineers and data scientists on modern ML fundamentals, production best practices, and architectural decision-making
What do you need?
- Production experience shipping deep learning models at scale - systems serving real traffic under latency constraints
- Ability to make architecture-level decisions independently - model selection, training infrastructure, serving strategy - and explain the reasoning and tradeoffs
- Experience designing ML systems with hard latency and throughput requirements, including optimisation decisions (quantization, pre-computed embeddings, batching strategies)
- Strong fundamentals in deep learning: gradient dynamics, attention mechanisms, graph message-passing, sequence modelling
- Track record of influencing technical strategy across teams - you don't just build, you shape direction
- Python, PyTorch (or equivalent), distributed training, ML pipeline orchestration
Nice to Have:
- Experience in FinCrime, fraud detection, AML, or regulated financial services
- Experience with graph-based methods (GNNs, entity resolution, link analysis) in production
- Foundation model fine-tuning or LLM evaluation experience
- Experience establishing modern ML practices in organisations scaling their ML capabilities
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: £145,000 - £182,000 + RSUs
Wise Benefits
#LI-AB3 #LI-Hybrid
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
- Production experience shipping deep learning models at scale, serving real traffic under latency constraints
- Ability to independently make architecture-level decisions across model selection, training infrastructure, and serving strategy
- Experience designing ML systems with strict latency and throughput requirements, including quantization, pre-computed embeddings, and batching strategies
- Strong fundamentals in deep learning, including gradient dynamics, attention mechanisms, graph message-passing, and sequence modeling
- Track record of influencing technical strategy across teams and shaping engineering direction
- Proficiency with Python, PyTorch or an equivalent framework, distributed training, and ML pipeline orchestration
- Experience in financial crime, fraud detection, anti-money laundering, or regulated financial services
- Production experience with graph-based methods, including graph neural networks, entity resolution, or link analysis
- Foundation model fine-tuning or large language model evaluation experience
- Experience establishing modern ML practices in organizations scaling their ML capabilities
Wise Compensation & Benefits Highlights
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Equity Value & Accessibility — Equity is broadly accessible through RSUs granted to all employees in addition to salary, aligning rewards with company performance. This company‑wide ownership stance is consistently highlighted in the benefits descriptions.
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Leave & Time Off Breadth — Paid time off is notably generous, with a global minimum of 33 days and 36 days listed for U.S. locations. After four years, a six‑week paid sabbatical plus a £1,000 stipend further strengthens time‑away benefits.
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Wellbeing & Lifestyle Benefits — Lifestyle support includes work‑from‑anywhere for up to 90 days per year after six months, flexible working principles, and a 24/7 Employee Assistance Program. Extras like three annual “Me Days” and a professional‑development allowance add quality‑of‑life value.
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
Hybrid Workspace
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.
