Detect fraud in milliseconds, at the moment a transaction happens, without blocking legitimate customers.
Machine Learning Engineer - Spain
Madrid or Barcelona, Spain · Permanent · Hybrid
What you'd actually work on
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
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 learning outputs with business rules and manual risk controls
Meeting explainability and traceability requirements for decisions that may need to be reviewed later
Rolling out new models safely through controlled testing, monitoring, and rollback mechanisms
What we're looking for
3+ years of experience developing applied machine learning models
Strong Python skills and good software engineering practices
Experience deploying and operating models in production
Knowledge of classification, anomaly detection, or risk-scoring methods
Experience working with imbalanced datasets and appropriate evaluation metrics
Understanding of precision, recall, false-positive rates, and the business trade-offs between them
Experience with model monitoring, drift detection, versioning, and retraining
Ability to work with large transactional or behavioural datasets
Experience with low-latency inference systems
Confidence working with risk, data engineering, platform, and product teams
Experience using Git, code reviews, automated testing, and CI/CD
Previous experience in fraud, payments, insurance, credit risk, or anomaly detection would be valuable, but it is not required if you have worked on comparable production machine learning problems.
The company
A fintech or insurtech scale-up operating across Southern Europe, with several hundred employees and high daily transaction volumes.
The machine learning team works closely with risk and engineering to improve fraud detection while limiting unnecessary friction for legitimate customers.
Health insurance, versátil working, and an equity plan.
Languages: Native or bilingual Spanish and professional English.
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