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 DescriptionWe’re looking for a Lead Machine Learning Engineer to join our growing Servicing Machine Learning and Data Engineering Team in London.
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.
Our team is responsible for 1) removing bottlenecks from Data Science workflows, 2) providing ML tooling for experiments, 3) developing Wise’s ML Label Platform. Moreover, we are responsible for driving high priority projects from proof-of-concept to MVP, to service / tooling.
We are looking for someone to own the evolution of ML experimentation tooling and label quality – 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 your 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
A bit about you:
Extensive experience with end-to-end distributed data systems, specially ML-centric ones;
Previous experience as Data Scientist in large scale product team / business;
Excellent Python and Software Engineering knowledge. Ability to work with Java if needed. Demonstrable experience collaborating with engineers on services.;
Strong drive to solve problems for Data Scientists, with the ability to work independently in a cross-functional and cross-team environment;
Good communication skills, ability to get the point across to non-technical individuals and back it up with data (and statistical analysis), to engage and manage project stakeholders;
Strong problem solving skills with the ability to help refine problem statements and propose solutions taking effort-impact-scalability tradeoff into account.
Some skills that will make you stand out:
Apache Spark, Iceberg, Kafka, dbt
Scikit-Learn, XGBoost, PyTorch, MLFlow,, GraphFrames, Ray
AWS (S3, EMR, SageMaker, Lakeformation), Terraform, Docker, GitHub CI/CD
Knowledge Graphs (+ RAG), graph ML, probabilistic programming, A/B testing
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.
Skills Required
- Extensive experience with end-to-end distributed ML-centric data systems
- Previous experience as a Data Scientist in large-scale product teams or businesses
- Excellent Python and software engineering knowledge
- Ability to work with Java if needed
- Demonstrable experience collaborating with engineers on services
- Good communication and stakeholder management; ability to present to non-technical audiences with data
- Strong problem-solving skills; able to refine problem statements and propose effort-impact-scalability tradeoffs
- Experience with testing, CI/CD, monitoring/alerting, and disaster recovery
- Apache Spark, Iceberg, Kafka, dbt
- Scikit-Learn, XGBoost, PyTorch, MLFlow, GraphFrames, Ray
- AWS (S3, EMR, SageMaker, LakeFormation), Terraform, Docker, GitHub CI/CD
- Knowledge Graphs, graph ML, probabilistic programming, A/B testing
What the Team is Saying
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Wise Compensation & Benefits Highlights
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Leave & Time Off Breadth — Global paid time off is presented as 33–36 days including local public holidays, plus three “Me Days,” with a paid six‑week sabbatical and stipend after four years. Work-from-anywhere for up to 90 days per year after six months further complements time away and flexibility.
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Equity Value & Accessibility — RSUs are granted to all employees, enabling broad ownership in the company. Equity is positioned as a standard, company‑wide component of total rewards.
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Parental & Family Support — Parental leave is described as generous across markets, with U.S. pages listing up to 18 weeks fully paid for birthing parents and 8 weeks for non‑birthing parents. Additional family‑oriented supports such as onsite Mother’s Rooms and abortion travel benefits are noted in certain locations.
Wise Insights
What We Do
Wise is a global technology company, building the best way to move and manage the world's money. With Wise Account and Wise Business, people and businesses can hold 40 currencies, move money between countries and spend money abroad. Large companies and banks use Wise technology too; an entirely new network for the world's money. Launched in 2011, Wise is one of the world’s fastest growing, profitable tech companies. In fiscal year 2025, Wise supported around 15.6 million people and businesses, processing over $185 billion in cross-border transactions and saving customers around $2.6 billion.
Why Work With Us
We’re truly global in who we are, how we work, and how we build. Everything we do is centred around creating a world of money that’s fast, easy, fair. And open to all. Everyone who works here owns a piece of Wise, from the work they do, to the stock they hold.
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Wise Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
We expect new joiners in the office most days to build connections and learn from colleagues for their first six months. After that, most Wisers split their working week between the office and home, typically coming in at least 12 times a month.
