The roles

We are looking for three highly motivated Machine Learning Scientists and Engineers to help develop world-leading machine learning models for weather and climate and efficient, scalable and sustainable software infrastructure that is ready for the Exascale Era. The work requires both technical expertise to create state-of-the-art and sustainable machine learning models and the required underlying software infrastructure, as well as excellent soft skills to work seamlessly in an international and interdisciplinary working environment. We aim to fill the following positions with the pool of applicants:

Role A: Machine Learning Scientist based in the machine learning modelling team. This position will focus on machine learning models for long time integrations, and therefore on advanced time-stepping techniques and physical consistency in long integrations.

Role B: Machine Learning Engineer based in the machine learning engineering team. This position will focus on machine learning workflows and workflow orchestration in the context of Anemoi and model integrations on EuroHPC systems and the AI Factories.

Role C: Machine Learning Scientist based in the machine learning modelling team. This position will support the development of a machine-learned Earth system model and required extensions of Anemoi, and will also consider model parallelisation approaches and HPC efficiency.

Your responsibilities

• Build a world-leading, efficient and sustainable software infrastructure for machine learning for ECMWF and the ECMWF Member and Co-operating States
• Explore new machine learning architectures and capabilities for Earth system modelling for operational weather predictions and climate modelling

The Teams

The positions will be located across the Machine Learning Modelling Team in the Earth System Modelling Section of the Research Department, and the Machine Learning Engineering Team in the Innovation Platform of the Forecast and Services Department.

The Machine Learning Modelling Team focuses on various aspects of machine learning for Earth system modelling. This includes developments for the operational Artificial Intelligence Forecasting System (AIFS), machine learning models to represent Earth system components beyond the atmosphere in a machine learned Earth System model, and methodological developments such as foundation modelling and generative machine learning.

The Machine Learning Engineering Team focuses on the infrastructure for machine learning at ECMWF to achieve sustainable and efficient model developments to serve ECMWF and the Member and Co-operating states. A main building block of this infrastructure is Anemoi as a framework for operational weather predictions based on machine learning and the support of the generation of machine learning datasets.

The advertised positions will work in very close collaboration with physical modellers and several other teams and sections within ECMWF and will have a direct impact to operational weather prediction at ECMWF and the creation of Digital Twins as part of Destination Earth.

At ECMWF, you will find a passionate community, collectively aiming to build world-leading global Earth system models for numerical weather prediction. This effort supports ECMWF’s strategy of producing cutting‐edge science and world-leading weather predictions and monitoring of the Earth system.

About the Projects/Programmes

The positions will be funded from three projects/programmes:

The Destination Earth initiative of the European Commission builds Digital Twins of the Earth and includes the development of a machine-learned Earth system model for weather and climate applications that covers the dominant components of the Earth system, including the ocean, ocean waves, sea-ice, land surface, and hydrology, leveraging the EuroHPC systems.

The EarthGenerator Horizon Europe project will extend the concept of the WeatherGenerator and explore foundation models for Earth sciences that can be applied to long prediction timescales of multiple years to decades.

The CLAIMA Horizon Europe project will compare different machine learning models for use cases in the area of climate modelling and exploit for this the power of Europe’s EuroHPC systems and AI factories. This includes the porting and running of the leading AI climate models on state-of-the-art High Performance Computing (HPC) architectures.

What we are looking for

Across all roles:

• Excellent analytical and problem-solving skills, with a proactive approach
• Excellent interpersonal and communication skills with the ability to collaborate effectively across interdisciplinary teams (machine learning engineers and scientists, domain scientists, operations) and external partners.
• Self-motivated and able to work with minimal supervision, but also dedicated and enthusiastic about teamwork with willingness to work in close collaboration.
• Ability to maintain effective communication and documentation of scientific results.
• Highly organised with the capacity to work on a diverse range of tasks to tight deadlines.
• Ability to work with standard software development tools (e.g. git) and to develop well-structured and maintainable software.
• Ability to work on and contribute effectively to large software projects following modern coding practices, including writing test and reviewing code.

Role A: Machine Learning Scientist (Modelling team)

• Experience with the development and evaluation of machine learning models, including model design, implementation, training, and scaling
• Experience with machine learning models for Earth system science and their evaluation; preferably for time ranges from months to years
• Experience in the development of machine learning models for sub-seasonal, seasonal, or climate simulations would be advantageous

Role B: Machine Learning Engineer (Engineering team)

• Ability to design, build, and maintain robust, reproducible machine learning pipelines
• Experience with dependency management and orchestration of complex, multi-step workflows
• Experience working in HPC or large-scale GPU computing environments (e.g. NVIDIA DGX systems, EuroHPC), including familiarity with job schedulers such as SLURM.
• Knowledge of large-scale dataset processing and integration would be beneficial
• Understanding of distributed training frameworks (e.g. PyTorch DDP) is a plus
• Familiarity with CI/CD pipelines or workflow orchestration tools (e.g., Airflow, Prefect) is a plus.

Role C: Machine Learning Scientist (Modelling team)

• Experience with the development and evaluation of machine learning models, including model design, implementation, training, and scaling
• Experience in designing for and optimizing performance of machine learning tools for training and inference
• Practical knowledge of scaling machine learning models to large HPCs with hundreds or thousands of nodes would be desirable
• Experience with the application of machine learning in the wider context of Earth system modelling would be an advantage
  
  

Your profile

• Advanced university degree (EQ7 level or above) in a physical, mathematical, data/machine learning or environmental science, or equivalent professional experience
• Experience in machine learning and/or machine learning engineering, including best practices for software development
• Experience in Earth system modelling is desirable but not mandatory
• Experience in HPC or large data science projects is desirable
• Candidates must be able to work effectively in English

If you feel that you have the relevant profile and motivation to join us but don't meet precisely all of the skills above, we still encourage you to apply!

About ECMWF

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 supercomputing 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.

Our vision: The strength of a common goal
Our mission: Deliver global numerical weather predictions focusing on the medium-range and monitoring of the Earth system to and with our Member States

ECMWF is a multi-site organisation, with its headquarters in Reading, UK, a data centre in Bologna, Italy, and a large presence in Bonn, Germany, as a central location for our EU-related activities. ECMWF is internationally recognised as the voice of expertise in numerical weather predictions for forecasts and climate science. For more details, see www.ecmwf.int.

Other information

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, either to Bonn, Germany, or to Reading, UK. 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 about a month of the vacancy closing date.

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

Who can apply

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 as well as nationals of European Union member 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.