Staff Applied ML Engineer - Financial Crime

Company Description

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 Description

About 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

I nterested? 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

• Wise Benefits

#LI-AB3 #LI-Hybrid

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 .