Machine Learning Engineer for Next Generation Triggers (NGT) (IT-CD-PI-2026-177-LD)

At CERN , the European Organization for Nuclear Research, physicists and engineers are probing the fundamental structure of the universe. Using the world's largest and most complex scientific instruments, they study the basic constituents of matter - fundamental particles that are made to collide together at close to the speed of light. The process gives physicists clues about how particles interact, and provides insights into the fundamental laws of nature. Introduction: You will develop and operate CERN's next-generation machine learning platform, enabling scientists and engineers to train, deploy and manage AI workloads at scale. Working with cutting-edge technologies including Kubernetes, GPUs and cloud-native tools, you will help build the infrastructure supporting machine learning for the High-Luminosity LHC. Functions: Develop and operate the NGT machine learning platform, covering model training, hyper-parameter optimisation and serving, targeting optimal usage of detector resources. Manage cluster resources, integrate new resources, on-premises or via external resources (such as clouds or HPC centers), and optimise resource usage (e.g. via CPU/GPU/RAM affinity layouts) and the existing MLOps workflows to cover deployment, observability and lifecycle management & versioning of models. Extend the cloud native infrastructure to support advanced workload scheduling, system monitoring & accounting, as well as prompt cost control. Produce and present reference architectures, documentation and best practices to support CERN users and use cases. Ensure efficient collaboration with other groups in `IT and in other departments, as well as constant alignment with the evolving landscape of external research and industry organisations. Master's Degree or equivalent relevant experience in the field of Computing Engineering or a related field. Experience: Knowledge in machine learning systems, ideally with hands-on experience deployment and operating such services in production environments. Familiarity with one or more of the following areas. Kubernetes, containerisation, CI/CD, observability and model lifecycle management. Experience using GPU and/or other accelerator platforms, including performance tuning and optimisation of inference workloads. Familiarity with open-source ecosystems and willingness to contribute, evaluate upstream projects and follow community best practices. Enthusiasm for learning new technologies and contributing to cutting-edge scientific computing is essential. Technical competencies: Knowledge of operating systems. Knowledge of system configuration tools. Architecture and design of ICT systems. Identification and selection of relevant emerging ICT technologies. Knowledge and application of software life-cycle tools and procedures. Behavioural competencies: Working in Teams: working well in groups and readily fitting into a team; participating fully and taking an active role in team activities. Solving Problems: addressing complex problems by breaking them down into manageable components. Managing Self: taking initiative beyond regular tasks and making things happen. Building Relationships: showing appreciation for the ideas and contributions of others and encourages others to express their views, even if controversial. Language skills: Spoken and written English, with a commitment to learn French. Eligibility and closing date: Diversity has been an integral part of CERN's mission since its foundation and is an established value of the Organization. Employing a diverse workforce is central to our success. We welcome applications from all Member States and Associate Member States . This vacancy will be filled as soon as possible, and applications should normally reach us no later than 11.09.2026 at 23:59 CEST. Employment Conditions: Contract type: Limited duration contract (2 years). Subject to certain conditions, holders of limited-duration contracts may apply for an indefinite position. Working Hours: 40 hours per week Job Flexibility: Hybrid This position involves: Work during nights, Sundays and official holidays, when required by the needs of the Organization. Job grade: 6-7 Job reference: IT-CD-PI-2026-177-LD Field of work: Software Engineering and IT Benchmark Job Title: Computing Engineer Global Benefits A competitive salary (tax free), increasing in line with your years of relevant experience. 30 days of paid leave per year plus 2 weeks annual closure. Coverage by CERN’s comprehensive health insurance scheme (for yourself, your spouse and children), and membership of the CERN Pension Fund. Family, child and infant monthly allowances depending on your individual circumstances. A relocation package (installation grant, removal, travel expenses) depending on your individual circumstances. Possibility to extend your contract up to 8 years + eligibility for indefinite contract tenure. Overview of CERN - Discover a world where the impossible is made possible! At CERN, the European Organization for Nuclear Research, we are pushing the frontiers of science and technology. Our groundbreaking work brings together not only physicists but also a diverse range of professionals from engineering, technical, scientific, and administrative fields. Together, we foster an environment where innovation and collaboration thrive. Every day, we face exciting new challenges and opportunities to contribute to cutting-edge research that shapes our understanding of the universe. We meet these challenges through the diverse perspectives within our teams, ensuring every contribution is valued and driving our shared sense of inclusion and purpose. Diversity is a core value of CERN since its foundation, and it remains central to our mission and continued success. If you are ready to be part of a dynamic, inclusive community pushing the boundaries of knowledge, CERN is the place where your curiosity and skills can thrive. Be part of our mission to uncover what lies at the heart of the universe! TAKE PART! More information about us, here: careers.cern