Senior Machine Learning Engineer - Servicing

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.

More about our mission and what we offer.

Job Description

Lead Machine Learning Engineer - Servicing and Data Engineering  

We’re looking for a Lead Machine Learning Engineer to join our growing Servicing ML and Data Engineering Team in Tallinn (or Budapest, Tallinn preferred). 

This role is a unique opportunity to scale and advance the impact of Data Science in Servicing tribe – namely Fincrime, KYC and Customer Support squads. What you build will have a direct impact on Wise’s mission and millions of our customers.

About the Role: 

Our team is responsible for:

1) removing bottlenecks from Data Science workflows,

2) providing ML tooling for experiments and pipelines,

3) developing Wise’s Feature Platform.

Moreover, we are responsible for driving high priority projects from proof-of-concept to MVP, to service / tooling, in order to unlock cross-team impact.

We are looking for someone to own the evolution of ML experimentation tooling and model retraining pipelines – at first for Fincrime teams, then for other squads in Servicing. You will co-own stakeholder management, roadmap, delivery and onboarding. You’re also expected to conduct presentations, demos and workshops, in addition to maintaining good documentation and progress updates for their projects. Additionally, you will have the freedom to drive impactful proof-of-concepts of new methodologies and tooling that bridge a gap for two or more teams in Servicing tribe.

Here’s how you’ll be contributing:

• Software engineering: e.g. testing + CI/CD, monitoring/alerting + disaster recovery

• MLOps: Terraform and AWS infra, ML governance for hundreds of models

• Data Engineering: distributed processing at terabyte scale

• Science: prove value of new methodologies / algorithms applied to cross-team domains, estimate and measure impact, mentor junior members in experiment design

Additional Information

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.

Keep up to date with life at Wise by following us on LinkedIn and Instagram.