Senior ML Engineering Team Lead - Financial Crime

Wise; Greater London; Vor Ort; GBP 135.000 - 175.000; Vollzeit

Zusammenfassung

Wise is hiring a Senior ML Engineering Lead to own the full model lifecycle for financial crime detection. You will architect a scalable, automated engineering ecosystem and build a team to execute on deploying hundreds of models with strict latency SLAs.

You will lead the Risk Modelling pillar in Wise's FinCrime organization, shaping architecture decisions, infrastructure design and cross-platform partnerships in a high-autonomy, low-hierarchy environment.

Qualifikationen

• You have led or built an ML Engineering or model lifecycle automation team at a high-growth company.
• You defined the standards that other engineering teams followed.

Aufgaben

• Architect the model factory and deployment pipeline for hundreds of models.
• Lead the Risk Modelling pillar within FinCrime, building a high-performance ML engineering team.
• Own end-to-end engineering strategy from architecture to hiring and cross-platform partnerships.
• Ensure low-latency, high-throughput ML systems with robust monitoring and audit trails.
• Recruit, mentor and develop senior ML engineers to scale the organization.

Kenntnisse

ML systems engineering

Model lifecycle automation

Team leadership

Architecture decisions

Hiring & mentoring

Jobbeschreibung

• Full-time
• Compensation: GBP 135,000 - GBP 175,000 - yearly

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 to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money.

As part of our team, you will be helping us create an entirely new network for the world's money.
For everyone, everywhere.

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

For everyone, everywhere. We're people building money without borders — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.

We're proud to have a truly international team, and we celebrate our differences.
Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.

If you want to find out more about what it's like to work at Wise visit Wise.Jobs .