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Mid LevelFull-timePythonRustMachine LearningAPIML
Salary Range
€55,000 - €70,000 /year
About the Role
We are looking for a Machine Learning Engineer to join our Data team at Eneba. This role focuses on building and iterating on real-time fraud/risk models to score transactions during checkout and support Risk decisioning.
Key Responsibilities
- →Build and iterate on real-time fraud/risk models (e.g., gradient boosting and anomaly detection approaches) to score transactions during checkout and support Risk decisioning.
- →Own the full ML lifecycle for fraud detection models: data exploration, feature engineering, training, evaluation, deployment, monitoring, and continuous improvement.
- →Design robust evaluation strategies for rare-event, highly imbalanced data, including handling delayed/partial ground truth and defining metrics aligned with business constraints.
- →Partner closely with Risk, backend, and Data/Platform teams to productionize models behind an API, integrate with the risk engine, and improve model-driven decision flows (pre-/post-authorization).
- →Drive experimentation and feedback-loop initiatives to improve labels and model quality over time, while maintaining strong reliability, observability, and documentation.
Requirements
- ✓3+ years of experience as a Machine Learning Engineer (or in a similar applied ML role), ideally working with risk/fraud, anomaly detection, credit/default modeling, or other rare-event classification problems.
- ✓Strong Python skills and hands-on experience building and iterating on supervised ML models (e.g., Gradient Boosting/LightGBM or similar), including feature engineering and model evaluation.
- ✓Proven ability to design and run robust experimentation and evaluation under real-world constraints (imbalanced data, delayed/late-arriving labels, noisy or partial ground truth).
- ✓Experience taking models to production and supporting the full model lifecycle (training, deployment, monitoring, and iteration) in collaboration with engineering teams.
- ✓Solid knowledge of ML metrics and decisioning (precision/recall, thresholding, calibration, offline vs. online performance) and how they translate into business outcomes.
- ✓Familiarity with modern MLOps tooling and practices (e.g., MLflow) and working with feature stores (Databricks Feature Store or alternatives).
Benefits
- ★Opportunity to join our Employee Stock Options program.
- ★Opportunity to help scale a unique product.
- ★Various bonus systems: performance-based, referral, additional paid leave, personal learning budget.
- ★Paid volunteering opportunities.
- ★Work location of your choice: office, remote, opportunity to work and travel.
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