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
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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
- Production experience shipping deep learning models at scale, serving real traffic under latency constraints
- Ability to make architecture-level decisions independently (model selection, training infrastructure, serving strategy)
- Experience designing ML systems with hard latency and throughput requirements, including optimization (quantization, pre-computed embeddings, batching)
- Strong fundamentals in deep learning: gradient dynamics, attention mechanisms, graph message-passing, sequence modelling
- Track record of influencing technical strategy across teams
- Experience with Python and PyTorch (or equivalent)
- Experience with distributed training and ML pipeline orchestration
- 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 scaling organisations
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.
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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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.
