Machine Learning Application Developer

• Support EUMETSAT internal and external users by creating data pipelines and curated ML-ready datasets with needed data content, quality, format, and structure for each use case;
• Develop, train, apply, and validate machine-learning models and other algorithms to transform Earth observation data into actionable information, particularly for Earth system feature detection;
• Explore and share the best ways to apply modern techniques and access Earth Observation data with the aim of helping the community to develop, train, and apply the models;
• Foster community development by encouraging and assisting EUMETSAT users in sharing commonly applicable parts of their applications in the form of how-to articles, code examples, infrastructure configurations, pre-trained models, datasets, etc.;
• Take an active role in user communities and support training courses, hackathons, and other community-oriented means of spreading practical knowledge to EUMETSAT member states.

Master’s or higher degree in a relevant computing or natural sciences subject., * Experience in ML model development and evaluation, preferably in weather and climate or Earth observation domain;

• Experience with at least one of the relevant ML libraries (Scikit Learn, TensorFlow, Keras, PyTorch…);
• Proven strong programming skills in at least Python and some experience in working with compiled languages and with modern collaborative software development practices, including Git-based development workflows, CI/CD pipelines, code review, and automated testing;
• Experience in working with large datasets;
• Experience in working in cloud environments;
• Eagerness to learn and expand one’s own knowledge, combined with ease in joining teams to solve problems and communicate resolutions;
• Excellent communication skills, with a proven ability to work effectively as part of a team, strong problem-solving skills, and a positive, can-do attitude.

The below skills and experience are seen as an advantage:

• Experience in working with Earth observation and weather and climate data;
• Experience in using machine learning pipelines and frameworks (MLFlow, Kubeflow, etc.);
• Contributions to the open-source software ecosystems., Candidates must be able to work effectively in English.

• Excellent salary, of up to Euro 8000 NET (after income tax) based on skills and experience;
• Flexible working time including additional flexi-leave;
• Full medical coverage for employee and family;
• Attractive pension;
• 30 days of annual leave + 14.5 days public holidays;
• Training and development support;
• Relocation allowance and support (if applicable).

EUMETSAT generates and uses data from its growing fleet of Earth observation satellites, together with data from partner agencies, to support weather forecasting-including nowcasting and numerical weather prediction-by meteorological and other environmental agencies in its Member States and worldwide. In addition, EUMETSAT supports climate monitoring by providing a wide range of satellite data, including rescued, recalibrated and reprocessed long-term series of satellite observations. EUMETSAT also supports its users in exploiting AI/ML by applying these methods to its core data products and through dedicated data curation activities., EUMETSAT’s role is to establish and operate meteorological satellites to monitor the weather and climate from space - 24 hours a day, 365 days a year. This information is supplied to the National Meteorological Services of the organisation’s Member and Cooperating States in Europe, as well as other users worldwide.

EUMETSAT also operates several Copernicus missions on behalf of the European Union and provide data services to the Copernicus marine and atmospheric services and their users.

As an intergovernmental European Organisation, EUMETSAT can recruit nationals only from the 30 Member States (Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, The Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey and the United Kingdom).