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, adaptable working, and an equity plan.

Languages:

Native or bilingual Spanish and professional English.

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