Machine Learning Engineer

Design and operate shared AI platforms used by experiments, researchers, and services across CERN, combining open-source innovation, cloud-native technologies, and high-performance computing to turn vast volumes of scientific data into insight. Support the Next Generation Triggers project and develop the central machine learning platform at CERN.

Responsibilities

• Design and develop the platform and support activities in the Next Generation Triggers (NGT) project, shaping the next generation systems for the High Luminosity LHC (HL-LHC) and beyond.
• Design and develop the central machine learning (ML) platform at CERN, supporting activities across all experiments, campus services and user communities.
• Collaborate with CERN teams to identify, prototype and benchmark AI use cases across heterogeneous computing infrastructures.
• Build and maintain an open-source-based AI/MLOps ecosystem, including model and agent lifecycle management, evaluation, monitoring, and engagement with open-source communities and industry partners.

Requirements

• By the application deadline, you have a maximum of 2 years of professional experience since graduation in the respective field and your highest educational qualification is either a bachelor’s or master’s degree.
• You must have a university degree and can’t hold a PhD.
• You have never had a CERN fellow or graduate contract before.
• Experience with machine learning systems and platforms, including practical operation of ML services.
• Strong knowledge of MLOps and cloud-native technologies, such as Kubernetes, containers, CI/CD, observability, and model lifecycle management.
• Experience with GPU or accelerator-based workloads, including performance optimisation for training and inference.
• Familiarity with open-source ecosystems, with an interest in evaluating and contributing to community projects.
• Your studies focused on Computing Engineering.
• Comfortable with Linux based systems.
• Knowledge of system configuration tools.
• Identification and selection of relevant emerging ICT technologies.
• Knowledge and application of software life-cycle tools and procedures.
• Spoken and written English, with a commitment to learn French.

Skills

• Machine Learning Systems
• ML Services Operation
• MLOps principles
• Cloud-native Technologies
• Kubernetes
• Containers
• CI/CD
• Observability
• Model Lifecycle Management
• GPU Workloads
• Accelerator-based Workloads
• Performance Optimization
• Open Source Ecosystems
• Linux Operating System
• System Configuration Tools
• Emerging Technologies
• Software Life-cycle Tools

Languages

English, French