Bull is a story of over a century of European innovation, focusing on powerful, sustainable, and sovereign digital solutions that let states and industries retain full control over their data and AI. Thousands of engineers, researchers and tech professionals shape the future of high‑performance computing, AI and quantum technologies, pushing boundaries from next‑generation HPC architectures to exascale supercomputers with world‑class R&D; and over 1,600 patents.
Role Description
We are searching for a Machine Learning Engineer to join Bull’s innovative R&D; team and contribute to AI‑driven solutions for infrastructure monitoring, reliability, and cybersecurity in HPC environments. The role focuses on leveraging large‑scale operational telemetry, metrics and logs to build predictive capabilities that improve system availability, detect anomalies and support proactive operations. The selected candidate will be responsible for model development, rigorous validation, operationalization and integration within a Kubernetes‑based platform.
Responsibilities
Design and develop ML/DL models for predicting hardware failures and detecting software or behavioral anomalies in HPC systems.
Apply advanced analytics techniques such as time‑series forecasting, anomaly detection, classification and predictive maintenance using large‑scale monitoring data.
Build and maintain data pipelines and features from infrastructure telemetry and logs.
Perform rigorous model validation to ensure robustness, reliability and production readiness.
Deploy and operationalize models within a Kubernetes‑based environment, including scalable inference services and lifecycle management.
Contribute to AI‑driven cybersecurity use cases, such as detecting abnormal behaviors, potential intrusions or security‑related anomalies in infrastructure and system activity.
Work within an Agile/Scrum environment, participating in sprint planning, stand‑ups and retrospectives.
Collaborate with system administrat