Senior Machine Learning Engineer II - FinCrime
Wise seeks a Senior Machine Learning Engineer II to strengthen the label integrity layer for its Risk ML and Intelligence team, ensuring accurate and auditable data for fraud detection models. The role focuses on label quality, monitoring, and statistical integrity to support reliable machine learning outcomes.
Skills required
About this role
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-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.
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: £111,000 - £145,000 + 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.
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