Senior ML Engineering Team Lead - Financial Crime

About the role:

Wise protects millions of customers and billions in transactions from fraud, money laundering and financial crime. Our ML systems are the front line of defense - operating at a global scale of 100K requests/minute under strict sub-50ms latency SLAs. We need an exceptional technical leader to own how these models are engineered, shipped and scaled.

We're hiring a Senior ML Engineering Lead to build and grow Wise's Risk Modelling engineering pillar. You will own the full model lifecycle standard for financial crime detection - from offline experimentation to production deployment and real-time monitoring and build the team to execute it. Your job is to build the automated engineering ecosystem and organisation that scales this safely to hundreds of models.

This is a rare greenfield leadership role with strong investment and engagement from Wise's CTO and senior leadership.

How we work:

Risk ML sits within Wise's FinCrime organisation, owning the full ML and AI foundation for financial crime detection. We're have three dedicated pillars - Feature Platform, Learning Loop and Risk Modelling. You'll lead the Risk Modelling pillar, leading a team of Senior ML Systems Engineers and Applied ML Engineers.

We operate with high autonomy and low hierarchy. You'll own the engineering strategy end-to-end - from architecture decisions and infrastructure design through to hiring, team culture and cross-platform partnerships. We value leaders who shape direction and build teams, not just manage delivery.

What will you be working on?:

The Model Factory: Architect the declarative pipeline that turns a configuration file into a deployed, monitored model - the engineering backbone for scaling to hundreds of models

The Experimentation Engine: Establish the reusable path from research (partnering with DS Research) to high-throughput production for traditional and modern architectures

Model Operations: Build the infrastructure for automated retraining, drift detection, threshold simulation/management and audit trails - the operational layer required to run hundreds of models safely at scale

The Team: Recruit, lead and mentor a world-class team of ML engineers. Establish a high-performance, engineering-first culture from scratch - setting hiring standards, technical bar and growth paths

Cross-Platform Partnership: Define and navigate the partnership with key platform teams - owning the build vs consume decisions for your pillar

What do you need?:

You've explicitly led or built an ML Engineering or model lifecycle automation team (not just used one) at a high-growth company - you defined the standards that other engineering teams followed

System-level and mathematical depth: you can design a model factory architecture, review a training pipeline & debug a runtime inference latency regression

Experience in high-throughput environments where latency constraints are tight and model failures carry massive financial consequences

Track record of hiring and developing senior engineers - you've built a team, not just inherited one

Ability to navigate ambiguity and make architecture-level decisions with incomplete information - this is a greenfield build, not an optimisation role

Strong enough technically to guide and review across deep learning, ML systems and production infrastructure - you lead through depth, not just delegation

Nice to Have:

Experience at a tier-1 fintech or payments company

Experience with graph-based methods (GNNs, entity resolution) in production

Foundation model fine-tuning or LLM evaluation experience

Experience establishing ML engineering practices in organisations transitioning from classical ML to deep learning

Interested? Find out more:

• How we work – a practical guide

• DEI @ Wise

• Wise Tech Stack (2025 update)

• See what it's like to work at Wise London!

• Our Engineering career map

• Wise Engineering –

What do we offer: 

• Starting salary: £135,000 - £175,000 + RSUs

• Wise Benefits

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