Detection EngineerBring every AI agent out of the box from google and crowdstrike to live.Join a global enterprise's cyber defense function as the engineer who brings machine learning into the detection workflow. The mission: move detection beyond static rules into predictive, model-driven threat identification — surfacing patterns, anomalies, and behavioral signals across EDR and SIEM telemetry. You'll partner closely with detection engineers, threat hunters, and cyber threat intelligence to catch what rule-based systems miss.What you'll doDesign, train, and deploy machine learning models for threat detection, with a focus on anomaly detection, behavioral analytics, and user/entity behavior analysis across endpoint and SIEM data sources.Build and maintain AI-driven detection pipelines capable of continuous learning as new threats emerge.Work with Detection Engineers to turn model outputs into explainable, actionable detection logic that SOC analysts can triage with confidence.Own feature engineering and training data curation across endpoint and SIEM telemetry, with attention to data quality, labeling accuracy, and statistical relevance.Identify and mitigate AI-specific risks, including model drift, biased training data, adversarial inputs, and automation over-reliance.Stay ahead of the evolving adversarial AI landscape — AI-generated phishing, evasive malware, deepfake-enabled social engineering — and build countermeasures into the detection stack.Bring an MLOps discipline to the role: version control for models, production performance monitoring, and ongoing evaluation of precision,



recall, and false-positive rates against team KPIs.What you'll bring3+ years applying machine learning to cybersecurity, fraud detection, or large-scale anomaly detection challenges.Strong Python fundamentals and hands‑on experience with ML frameworks such as scikit‑learn, Py Torch, Tensor Flow, or XGBoost, spanning both classical and deep learning methods.Practical familiarity with endpoint detection and response tools, SIEM platforms, and an understanding of how detection pipelines consume model output.Solid grounding in feature engineering, model evaluation, and handling class imbalance.Exposure to MLOps practices: model versioning, monitoring, and retraining workflows, using tools like MLflow, Vertex AI, or similar.Awareness of adversarial ML risks (model poisoning, evasion techniques, prompt injection) and mitigation approaches in live environments.What's on offerHybrid working model balancing in‑office collaboration and remote flexibility, including the option to work abroad for part of the year.Performance‑based rewards: bonus scheme, pension contribution, employee share program, and additional discounts (varies by location).Ongoing career development, learning and training budget.Versátil work arrangements plus health, wellbeing, and family‑related benefits, including support for parental leave and returning from career breaks."You run it - you build it" mindset. Take ownership, bring your ideas, improve security inside the company, tackle new challenges and grow in your career inside the company.

📌 Detection Engineer (Madrid)
🏢 Montash
📍 Madrid