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 DescriptionAbout the role: We are looking for an IC3 Machine Learning Engineer to join our Risk ML and Intelligence team. In this role, you will be key to enabling the building of our machine learning models by focusing on the label side, building the integrity layer for our label platform.
Every machine learning model at Wise learns from two core components: features (user signals) and labels (historical tags for activity like money laundering or fraud). If our labels are inaccurate, our models learn the wrong behavior. You will be responsible for label side quality, label monitoring, statistical integrity, and designing robust audit processes to ensure our ML infrastructure learns from clean, reliable data.
How we work: At Wise, we operate with autonomous, cross-functional teams that put the customer first. We believe strong engineers can learn and adapt across tech stacks, so our interview and pair programming evaluations are language-agnostic (focused on Python or Java), allowing you to solve complex technical problems in the environment you are most comfortable with.
What will you be working on?:
Building, scaling, and maintaining the integrity layer of our label platform for Risk ML models.
Defining, implementing, and monitoring statistical fundamentals and key quality metrics for data and labels.
Designing automated audit processes to evaluate and monitor label quality over time.
Working end-to-end on machine learning model training, evaluation, and pipeline deployment.
Collaborating closely with cross-functional partners across Risk Intelligence, Data Engineering, and Product.
What do you need?:
Education: A degree in STEM (Computer Science, Mathematics, Statistics, Physics, Chemistry, Electrical Engineering, or a related quantitative field).
Statistical Integrity: Strong mathematical and statistical fundamentals with a proven track record of applying statistical analysis to complex data environments.
ML Lifecycle Expertise: Hands-on experience working across model training, evaluation, and deployment (utilizing frameworks around Machine Learning, AI, Neural Networks, or NLP).
Programming Skills: Strong proficiency in Python or Java for data scripting and production engineering, alongside advanced SQL capability.
Data Fundamentals: Solid hands-on experience building static data pipelines, conducting deep-dive data analysis, and using data visualization tools to understand statistical behavior.
Nice to Have:
Proven success in competitive machine learning environments or platforms (e.g., Kaggle, KDD competitions, or Google Summer of Code / GSoC).
Experience with specialized ML architectures such as Graph Neural Networks (GNNs), Support Vector Machines (SVM), Natural Language Processing (NLP), or Transformers/LSTMs.
Familiarity with real-time streaming data pipelines (e.g., Kafka).
Domain experience within Fintech, E-commerce, or fast-scaling tech companies.
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 – https://medium.com/wise-engineering
What do we offer:
Starting salary: £87,500 – £111,000 + stock equity grants (RSUs vesting over 4 years) + benefits.
Wise Benefits
Interested in more than one role? If you're interested in multiple roles, please apply for just one—the one you're most excited about. If you submit multiple applications requiring the same assessment, we'll continue your recruitment journey using your first application, and any duplicate applications will be automatically closed. This helps ensure a fair and consistent interview process. If another role feels like a better fit, you can discuss this with your recruiter during the process.
#LI-AB3 #LI-Hybrid
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
- Degree in a STEM field (Computer Science, Mathematics, Statistics, Physics, Chemistry, Electrical Engineering, or related quantitative field)
- Strong mathematical and statistical fundamentals with applied experience in complex data environments (statistical integrity)
- Hands-on experience across ML lifecycle: model training, evaluation, and deployment
- Proficiency in Python or Java for data scripting and production engineering
- Advanced SQL capability
- Experience building static data pipelines, deep-dive data analysis, and using data visualization tools to understand statistical behavior
- Proven success in competitive machine learning platforms (e.g., Kaggle, KDD, GSoC)
- Experience with specialized ML architectures (GNNs, SVMs, NLP, Transformers/LSTMs)
- Familiarity with real-time streaming data pipelines (e.g., Kafka)
- Domain experience within Fintech, E-commerce, or fast-scaling tech companies
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.
