Senior Machine Learning Engineer II - FinCrime

• Join the Risk ML and Intelligence team as an IC3 Machine Learning Engineer. • Focus on the label side of machine learning, building the integrity layer for Wise’s label platform. • Work on the component that decides what models actually learn, since every model at Wise learns from features and labels, and inaccurate labels teach the wrong behaviour. • Take responsibility for label quality, label monitoring, statistical integrity, and robust audit processes. • Make sure the ML infrastructure learns from clean, reliable data on activity such as money laundering and fraud. • Work in autonomous, cross-functional teams that put the customer first. • Solve problems in the environment you are most comfortable with, as interviews and pair programming are language-agnostic across Python or Java. • Work from London on a hybrid basis.

• Hold a degree in a STEM field such as Computer Science, Mathematics, Statistics, Physics, Chemistry, Electrical Engineering, or a related quantitative discipline. • Bring strong mathematical and statistical fundamentals with a proven record of applying statistical analysis to complex data environments. • Bring hands-on experience across model training, evaluation, and deployment using frameworks around machine learning, AI, neural networks, or NLP. • Work with strong proficiency in Python or Java for data scripting and production engineering. • Bring advanced SQL capability. • Build static data pipelines and conduct deep-dive data analysis. • Use data visualisation tools to understand statistical behaviour.

• Show proven success in competitive machine learning environments such as Kaggle, KDD competitions, or Google Summer of Code. • Bring experience with specialised ML architectures including Graph Neural Networks, Support Vector Machines, NLP, or Transformers and LSTMs. • Bring familiarity with real-time streaming data pipelines such as Kafka. • Bring domain experience from fintech, e-commerce, or fast-scaling tech companies. • Apply for just one Wise role, the one you are most excited about, as duplicate applications requiring the same assessment are automatically closed.

• Build, scale, and maintain the integrity layer of the label platform for Risk ML models. • Define, implement, and monitor statistical fundamentals and key quality metrics for data and labels. • Design automated audit processes that evaluate and monitor label quality over time. • Work end-to-end on machine learning model training, evaluation, and pipeline deployment. • Collaborate closely with cross-functional partners across Risk Intelligence, Data Engineering, and Product.

We have been Wise customers for years and have always had a great experience. We love their transparent approach in everything they do, from showing the correct exchange rates to disclosing all their fees, and even being honest about how they work. Founded in 2010 in London, Wise is now a publicly traded company operating in over 160 countries, generating 1.2+ billion pounds in revenue (2025) and is highly profitable. What we especially love at Wise is their work from anywhere policy after 6 months: Basically you can remotely work from (almost) anywhere in the world for up to 90 days a year. As they are solving a massive problem and are strongly mission-driven, we are sure that any individual passionate about working on complex, impactful problems in sending money globally efficiently, while continuing to work on the much-loved Wise solutions, will love working there.

• Earn a starting salary of £111,000 to £145,000 a year. • Receive stock equity grants as RSUs vesting over four years, plus benefits. • Own the integrity layer that every Risk ML model at Wise depends on. • Work end-to-end across training, evaluation, and deployment rather than a single slice of the lifecycle. • Interview language-agnostically in Python or Java, whichever suits you best. • Progress along Wise’s published engineering career map. • Work hybrid from the London office in an autonomous, cross-functional team. • Join a truly international team that treats diversity, equity, and inclusion as central to how it works.

Wise is a global technology company building the best way to move and manage the world’s money, with minimum fees, maximum ease, and full speed. Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise exists to make their lives easier and save them money. Its Risk ML and Intelligence team builds the machine learning behind financial crime and fraud detection, where label quality directly determines what those models learn. Wise operates with autonomous, cross-functional teams that put the customer first, and believes strong engineers can learn and adapt across tech stacks. It builds money without borders and without judgement or prejudice.