Senior Machine Learning Engineer - FinCrime

Wise; Greater London; On-site; GBP 88,000 - 111,000; Full time

Benefits offered by this job

Stock equity (RSUs)

Benefits

Job summary

Wise is seeking an IC3 Machine Learning Engineer in London to improve the label integrity layer for Risk ML models. You will focus on data quality, auditing, and robust statistical methods to ensure reliable model learning from clean data.

You will work with cross-functional partners across Risk Intelligence, Data Engineering, and Product, deploying end-to-end ML pipelines and maintaining high data standards in a fast-moving fintech environment.

Qualifications

• Degree in STEM fields such as CS, math, statistics, physics, chemistry, or related quantitative field.
• Strong mathematical and statistical fundamentals with applied data analysis experience.
• Hands-on ML lifecycle experience across training, evaluation, and deployment (ML/AI/NLP).
• Proficiency in Python or Java for data scripting and production engineering, plus SQL.
• Experience building static data pipelines and using data visualization to understand statistics.

Responsibilities

• Build, scale, and maintain the integrity layer of the label platform for Risk ML models.
• Define, implement, and monitor statistical fundamentals and quality metrics for data and labels.
• Design automated audit processes to evaluate label quality over time.
• Work end-to-end on ML model training, evaluation, and pipeline deployment.
• Collaborate with Risk Intelligence, Data Engineering, and Product teams.

Skills

Statistical analysis

ML lifecycle

Python/Java

SQL

Data visualization

Education

STEM degree

Tools

Kafka

Job description

Company Description

Wise is a global technology company, building the best way to move and manage the world’s money.

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.

Job Description

More about our mission and what we offer.

About 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.

Qualifications

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‑dived 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.

Additional Information

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

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.