Detect fraud in milliseconds, at the moment a transaction happens, without blocking legitimate customers.

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Machine Learning Engineer - Spain
Madrid or Barcelona, Spain · Permanent · Hybrid

What you'd actually work on

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- Building and maintaining fraud detection and risk-scoring models used on live transactions
- Developing features from transactional, behavioural, account, and device data
- Training and evaluating models against new and evolving fraud patterns
- Deploying models into low-latency production systems
- Reducing false positives while maintaining effective fraud detection rates
- Working with risk specialists to translate fraud scenarios and business rules into model features
- Designing feedback loops using confirmed fraud cases, manual reviews, and transaction outcomes
- Monitoring model performance, feature quality, drift, latency, and prediction distributions




- Investigating model degradation and changes in customer or fraud behaviour
- Improving model deployment, versioning, retraining, and rollback processes
- Documenting model behaviour and decisions for engineers, risk teams, and auditors
- Contributing to code reviews, automated testing, CI/CD, and ML engineering standards

Where it gets technically interesting

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- Real-time inference under strict latency constraints, with decisions required before transactions are completed
- Highly imbalanced datasets where confirmed fraud represents only a small proportion of all transactions
- Fraud patterns that change deliberately in response to existing detection methods
- Managing delayed or incomplete labels when transaction outcomes are not immediately known
- Balancing fraud detection rates against the commercial and customer impact of false positives
- Identifying drift in models and features before it results in significant financial losses
- Combining machine

📌 Machine Learning Engineer (Madrid)
🏢 The French Sourcer
📍 Madrid