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.
Responsibilities
•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
Requirements
•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
Conditions
Starting salary: £145,000 - £182,000 + RSUs
