Job Description
Job Description
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hackajob is collaborating with Wise to connect them with exceptional professionals for this role.
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Please read the following job description thoroughly to ensure you are the right fit for this role before applying.
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Company Description
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Wise is a global technology company, building the best way to move and manage the world's money.n Min fees. Max ease. Full speed.
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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.
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As part of our team, you will be helping us create an entirely new network for the world's money.n For everyone, everywhere.
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More about our mission and what we offer.
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Job Description
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About the role:
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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.
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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.
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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.
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How we work:
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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.
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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.
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What will you be working on?
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- 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
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Defining the architecture strategy for how Wise applies modern ML to risk – which model families, which serving patterns, which training paradigmsBuilding the reusable end-to-end pipeline pattern – from experimentation through training to production deployment – that future models followEvaluating and prototyping foundation model and embedding approaches for transaction representation across FinCrime domainsPartnering with Data Science on model evaluation, experimentation design and causal measurement in domains where clean A/B testing isn't always possibleMentoring engineers and data scientists on modern ML fundamentals, production best practices, and architectural decision-makingn
What do you need?
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- Production experience shipping deep learning models at scale – systems serving real traffic under latency constraints
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Ability to make architecture-level decisions independently – model selection, training infrastructure, serving strategy – and explain the reasoning and tradeoffsExperience 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 modellingTrack record of influencing technical strategy across teams – you don't just build, you shape directionPython, PyTorch (or equivalent), distributed training, ML pipeline orchestrationn
Nice to Have:
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- Experience in FinCrime, fraud detection, AML, or regulated financial services
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Experience with graph-based methods (GNNs, entity resolution, link analysis) in productionFoundation model fine-tuning or LLM evaluation experienceExperience establishing modern ML practices in organisations scaling their ML capabilitiesn
Interested? Find out more:
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- How we work – a practical guide
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DEI @ Wise Wise Tech Stack (2025 update) See what it's like to work at Wise London! Our Engineering career map Wise Engineering – do we offer: n
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- Starting salary: £145,000 – £182,000 + RSUs
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Wise Benefitsn
#LI-AB3 #LI-Hybrid
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Additional Information
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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.
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We're proud to have a truly international team, and we celebrate our differences.n 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.
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If you want to find out more about what it's like to work at Wise visit Wise.Jobs. xohmjla
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Keep up to date with life at Wise by following us on LinkedIn and Instagram.
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