Senior ML Engineering Lead - Financial Crime

• Own how Wise’s financial crime ML models are engineered, shipped, and scaled, protecting millions of customers and billions in transactions. • Work on systems that sit on the front line of defence, running at 100K requests a minute under strict sub-50ms latency SLAs. • Build and grow Wise’s Risk Modelling engineering pillar from scratch. • Own the full model lifecycle standard for financial crime detection, from offline experimentation through production deployment to real-time monitoring. • Build the automated engineering ecosystem and organisation that scales this safely to hundreds of models. • Lead a team of Senior ML Systems Engineers and Applied ML Engineers within the FinCrime organisation, alongside the Feature Platform and Learning Loop pillars. • Take on a rare greenfield leadership role with strong investment and engagement from Wise’s CTO and senior leadership. • Work from London on a hybrid basis, with high autonomy and low hierarchy.

• Have explicitly led or built an ML Engineering or model lifecycle automation team at a high-growth company, rather than simply used one. • Have defined the standards that other engineering teams then followed. • Bring system-level and mathematical depth, enough to design a model factory architecture, review a training pipeline, and debug a runtime inference latency regression. • Bring experience of high-throughput environments with tight latency constraints, where model failures carry massive financial consequences. • Show a track record of hiring and developing senior engineers, having built a team rather than inherited one. • Navigate ambiguity and make architecture-level decisions with incomplete information, as this is a greenfield build rather than an optimisation role. • Stay technically strong enough to guide and review across deep learning, ML systems, and production infrastructure, leading through depth rather than delegation.

• Bring experience at a tier-1 fintech or payments company. • Bring production experience with graph-based methods such as GNNs and entity resolution. • Bring foundation model fine-tuning or LLM evaluation experience. • Have established ML engineering practices in organisations moving from classical ML to deep learning. • Enjoy owning build versus consume decisions in partnership with platform teams.

• Architect the declarative pipeline that turns a configuration file into a deployed, monitored model, forming the engineering backbone for scaling to hundreds of models. • Establish the reusable path from research through to high-throughput production, partnering with DS Research across traditional and modern architectures. • Build the infrastructure for automated retraining, drift detection, threshold simulation and management, and audit trails. • Create the operational layer needed to run hundreds of models safely at scale. • Recruit, lead, and mentor a world-class team of ML engineers. • Establish a high-performance, engineering-first culture from scratch, setting hiring standards, the technical bar, and growth paths. • Define and navigate partnerships with key platform teams, owning the build versus consume decisions for your pillar. • Own the engineering strategy end to end, from architecture and infrastructure design through to hiring and team culture.

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 £135,000 to £175,000 a year plus RSUs. • Take a greenfield leadership role with strong investment and engagement from the CTO and senior leadership. • Build a pillar and a team from scratch rather than inheriting either. • Work on ML systems running at 100K requests a minute under sub-50ms latency SLAs. • Operate with high autonomy and low hierarchy, shaping direction rather than just managing delivery. • Progress along Wise’s published engineering career map. • Work hybrid from the London office. • 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 group sits within the FinCrime organisation and owns the full ML and AI foundation for financial crime detection across three pillars: Feature Platform, Learning Loop, and Risk Modelling. The teams operate with high autonomy and low hierarchy, valuing leaders who shape direction and build teams. Wise builds money without borders and without judgement or prejudice.