Research Software Engineer – AI and Earth Observation

Research Software Engineer – AI and Earth Observation

The Jülich Supercomputing Centre (JSC) operates one of the most powerful computer systems for scientific and technical applications in Europe and makes it available to scientists at Forschungszentrum Jülich, in Germany and across Europe for research purposes via an independent peer-review process. As part of this remit, the JSC carries out research and development work in the fields of technology, HPC systems, communications, highly scalable data science, mathematics and application support.

The Large Scale Data Science department develops machine learning techniques and other methods and tools for the management, analysis and modelling of large-scale data, and for the integration of data and computing resources into federated HPC infrastructures. The models, tools and methods are developed in collaboration with users in selected scientific domains, tested, scaled for the pre-zettascale era and offered as generic solutions to a large number of scientific communities.

In this position, you will join our Simulation and Data Lab for AI and Machine Learning for Remote Sensing. The lab advances interdisciplinary research and operational services by combining geoscience and remote sensing methods with AI and advanced computing technologies for Earth observation (EO) applications. Your work will focus on deploying, operating and scaling scientific software, AI services and data workflows across supercomputing, cloud and quantum computing environments.

You will work closely with our researchers and international partners from academia, industry and public agencies, bringing together scientific computing, data engineering and AI to address EO challenges.

Specifically, you will:

• Design, deploy and operate end-to-end federated EO and AI workflows, from multisource data collection and pre-processing to model training, serving and delivery of actionable information
• Build and maintain containerised and cluster-based environments for AI services, including deployment pipelines, monitoring, logging and alerting
• Develop, port and optimise scientific software and workflows across HPC, cloud and hybrid quantum-classical environments, ensuring performance, scalability, portability and reliability
• Apply sustainable software engineering and MLOps practices, including CI/CD, automated testing, packaging, versioning and documentation, to support reproducibility and maintainability
• Develop and document stable APIs and accessible interfaces that help users integrate EO and AI services into their operational workflows
• Collaborate with researchers and infrastructure teams to align applications with available computing, storage and networking resources, and support the adoption of HPC and AI services
• Contribute expertise in advanced analytics to research initiatives and the preparation of proposals for national and international funding calls and tenders
• Contribute to open-source projects, technical reports, publications, presentations and educational activities, including courses, hackathons and community events

Your qualifications and technical background include:

• An excellent master’s degree (and a PhD) in computer science, software engineering, data science or a related field
• At least a few years of industry experience in software engineering, data engineering, scientific computing or AI engineering, with hands‑on experience delivering and supporting production software, data‑intensive workflows or computing services
• Strong programming skills and practical experience with Linux, containerisation, version control and service deployment on HPC systems or cloud infrastructure, including GPU‑accelerated workloads
• Experience with deep learning…