Wise
Staff Applied ML Engineer - Financial Crime
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Staff Applied ML Engineer
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 across borders, spending abroad, or making international payments, Wise mission is to make their lives easier and save them money. Together, we’re creating the next generation of transaction networks.
About This Role
Wise processes billions of transactions yearly, with critical decisions—and risks—underpinning each one: Is this transaction safe? Our ML systems answer this at scale, in real-time, and across every market. The Risk ML team is pioneering the next era of financial crime detection at Wise, leveraging modern architectures like deep learning, graph neural networks, and foundation models to identify sophisticated fraud and money laundering.
As a Staff Applied ML Engineer, you’ll lead this evolution by defining the architecture strategy, shipping neural models at production scale, and establishing scalable blueprints for fincrime domains. This is a greenfield opportunity—you’ll set the direction for Wise’s advanced ML in financial crime risk, backed by senior leadership and investments.
How We Work
The Risk ML team operates as a specialised unit within Wise FinCrime, owning the full ML and AI foundation for financial crime detection. Currently scaling into three pillars:
- Feature Platform
- Learning Loop
- Risk Modelling.
Joining the Risk Modelling team, you'll collaborate with data scientists, platform engineers, product experts, and domain specialists. Here, you’ll thrive in a high-autonomy, low-hierarchy culture—driving end-to-end ownership, from innovation and architecture to production deployment and impact measurement. Your role is to shape, not just execute.
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Key Responsibilities
You’ll oversee and deliver:
- Architected, trained, and deployed advanced ML and deep learning models (sequence-aware, graph-based, attention-driven) for real-time financial crime detection, scaled to Wise’s operational load.
- Defined the long-term technical strategy for applying modern ML to fincrime risk, covering:
- Model family prioritisation
- Serving strategies
- Training paradigms (e.g., A/B testing vs causal inference)
- Built and standardised reusable end-to-end ML pipelines, from experimental validation to production deployment.
- Evaluated foundation/fine-tuned models and embedding approaches across transaction graphs and domains.
- Partnered with data scientists to design experiments, benchmark models, and enforce causal measurement discipline (where randomised testing is impractical).
- Mentored junior engineers and practitioners on modern ML fundamentals, production best practices, and architectural trade-offs.
Who You Are (Requirements)
Core
- Proven track record shipping deep learning models at scale to serve live traffic under latency constraints.
- Ability to drive architecture decisions (model selection, training paradigms, serving strategies) independently, explaining technical trade-offs transparently.
- Deep experience designing low-latency, high-throughput ML systems, including:
- Model optimisations (e.g., quantisation, pre-computed embeddings, batching strategies)
- Distributed training and orchestration techniques.
- Solid understanding of deep learning foundations, including:
- Gradient optimisation
- Attention mechanisms
- Graph neural networks
- Sequence modelling.
- Strategic influence: a track record of technical leadership, shaping ML roadmaps across collaborating teams—more than just hands-on coding.
- Hands-on skills in:
- Python, PyTorch (or equivalent frameworks)
- Distributed training infrastructures
- CI/CD-enabled ML pipelines.
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Nice-to-have
- Contextual expertise in:
- Financial crime detection
- AML (Anti-Money Laundering)
- Regulated financial services.
- Production use of graph-based technologies (GNNs, entity resolution, link analysis).
- Experience fine-tuning foundation models or evaluating large language models.
- Previous roles introducing modern ML practices in scaling data-driven organisations.
What Do We Offer?
- Salaried range: £145,000–£182,000, plus annual Restricted Share Units (RSUs) as part of our equity package.
- Full benefits: See Wise Benefits (locations vary).
Why It Matters
“For everyone, everywhere.” Wise’s mission is to redefine money—without borders, prejudice, or exclusion. We thrive as a diverse, inclusive team. Our international culture relies on mutual respect, psychological safety, and equitable career pathways. If you believe differentiated talent drives innovation, apply to be part of it.
Interested? Explore further:
- How we operate – a practical guide
- Diversity, Equality & Inclusion at Wise
- Wise’s Engineering Career Map
- Life at Wise London or Our product + engineering teams (global)
Visit our careers page for updates and to apply.
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