Machine Learning Engineer

We are in search of a highly motivated Machine Learning Engineer (A2) to work with ECMWF and its Member States on the next generation of machine learning weather forecasting models. This role is an integral part of a dynamic team, consisting of scientists and software engineers contributing to building ECMWF’s next generation of weather forecasting systems.

At ECMWF, you will join a passionate community collectively aiming to bring novel technology and science to the cutting‑edge of numerical weather prediction. With the recent breakthrough in Artificial Intelligence (AI) and the progress made in AI‑driven weather forecasting, it becomes clear that AI will play a key role in the next generation of forecasting systems. To this end, ECMWF built a dedicated multi‑disciplinary group to tackle these challenges. ECMWF has been the first operational weather centre to publish results of its own global machine‑learning weather model – the Artificial Intelligence Forecasting System (AIFS).

We are working with our Member States to build a high‑level machine learning framework to train data‑driven weather forecasting models, called Anemoi (https://anemoi.readthedocs.io/en/latest/). For ECMWF, the AIFS is one possible product from this system, which will enable meteorological organisations to provide data sources and recipes to train forecasting models. This concept has already been demonstrated (https://www.ecmwf.int/en/about/media-centre/aifs-blog/2024/data-driven-regional-modelling).

In this role, you will contribute to the development of the ECMWF open‑source software stack, particularly Anemoi, working with scientists and users at ECMWF and in the Member States to design and implement machine‑learning components for operational weather forecasting. You will support the development of machine learning components for training and inference, ensuring software is robust and scalable for operational use, and engage with the open‑source community to improve usability and maintainability. The role involves close collaboration with Member State teams and may include travel.

The role sits in the Machine Learning Engineering team, within the Innovation Platform. The primary focus of the team is to ensure that ECMWF’s machine‑learning tools are robust, scalable, and suitable for operational weather forecasting, while adapting to rapid scientific advances in data‑driven forecasting. The team develops and maintains production‑ready ML frameworks in close collaboration with scientists and engineers at ECMWF and in the Member States, ensuring they can be used reliably in operational and research environments. By continuously evolving the software and workflows, the team aims to keep ECMWF at the forefront of global weather prediction.

The European Centre for Medium‑Range Weather Forecasts (ECMWF) is a world leader in Numerical Weather Predictions providing high‑quality data for weather forecasts and environmental monitoring. As an intergovernmental organisation, we collaborate internationally to serve our members and the wider community with global weather predictions, data and training activities that are critical to contribute to safe and thriving societies.

The success of our activities depends on the funding and partnerships of the 35 Member and Co‑operating States who provide the support and direction of our work. Our talented staff together with the international scientific community, and our powerful super‑computing capabilities, are the core of a 24/7 research and operational centre with a focus on medium and long‑range predictions. We also hold one of the largest meteorological data archives in the world.

ECMWF has also developed a strong partnership with the European Union and has been entrusted with the implementation and operation of the Destination Earth Initiative and the Climate Change and Atmosphere Monitoring Services of the Copernicus Programme and the Strengthening Early Earning in Africa (SEWA) Programme. Other areas of work include High Performance Computing and the development of digital tools that enable ECMWF to extend provision of data and products covering weather, climate, air quality, fire and flood prediction and monitoring.

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• Actively contribute to the ECWMF open‑source software stack, particularly designing, implementing, and maintaining features in Anemoi core and inference pipelines.
  
• Collaborate with scientists and users to translate research ideas into production‑ready ML systems.
  
• Contribute to open‑source development, including code reviews, documentation, and community interaction.
  
• Ensure models and software meet operational reliability, scalability, and performance requirements.
  
• Provide support to Member States to build their machine learning weather forecasting models.
  

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• Excellent analytical and problem‑solving skills with a proactive, continuous improvement approach.
  
• Initiative and ability to work collaboratively, with other ECMWF teams and external collaborators, but also able to work independently.
  
• Ability to work effectively in interdisciplinary teams (ML engineers, domain scientists, operations).
  
• Ability to maintain a supportive and user‑focused approach.
  
• Good interpersonal and communication skills.
  
• Willingness to travel across Europe multiple times per year.
  
• Dedication, passion, and enthusiasm to succeed both individually and across teams of developers.
  
• Highly organised with the capacity to work on a diverse range of tasks to tight deadlines.
  

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• Experience in machine learning workflows, including training and inference pipelines.
  
• Demonstrated experience developing object‑oriented software in Python.
  
• Experience contributing to large‑scale software projects, preferably open source related to machine learning and/or involving multiple software components.
  
• Experience dealing with users, gathering feedback and planning developments.
  
• Knowledge of model versioning, experiment tracking, and reproducibility.
  
• Experience with CI/CD pipelines and test‑driven development would be an advantage.
  
• Experience designing and maintaining robust configuration systems and well‑defined APIs, including the use of data validation and modelling tools such as Pydantic would be an advantage.
  
• Experience developing software for high‑availability operational environments would be an advantage.
  
• Capability to develop scientific software to process large datasets, including familiarity with large multidimensional scientific data formats such as NetCDF, GRIB, Zarr, or HDF5 would be desirable.
  

Grade remuneration: The successful candidates will be recruited according to the scales of the Co‑ordinated Organisations. Details of salary scales and allowances are available on the ECMWF website at www.ecmwf.int/en/about/jobs.

Starting date: as soon as possible.

Candidates are expected to relocate to the duty station. As a multi‑site organisation, ECMWF has adopted a hybrid organisation model which allows flexibility to staff to mix office working and teleworking, including away from the duty station (within the area of our member states and co‑operating states).

Interviews by videoconference (MS Team) are expected to take place shortly after the vacancy closing date.

Successful applicants and members of their family forming part of their households will be exempt from immigration restrictions.

Applicants are invited to complete the online application form by clicking on the apply button below.

At ECMWF, we consider an inclusive environment as key for our success. We are dedicated to ensuring a workplace that embraces diversity and provides equal opportunities for all, without distinction as to race, gender, age, marital status, social status, disability, sexual orientation, religion, personality, ethnicity and culture. We value the benefits derived from a diverse workforce and are committed to having staff that reflect the diversity of the countries that are part of our community, in an environment that nurtures equality and inclusion.

Applications are invited from nationals from ECMWF Member States and Co‑operating States. In these exceptional times, we also welcome applications from Ukrainian nationals for this vacancy. Applications from nationals from other countries may be considered in exceptional cases.

ECMWF Member States and Co‑operating States are: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Morocco, the Netherlands, Norway, North Macedonia, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Türkiye and the United Kingdom.