Machine Learning Scientist
- Hiring from
- Germany, United Kingdom
- Work type
- Hybrid
- Posted
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Your role
We are looking for a highly motivated Machine Learning Scientist to help develop world-leading machine learning models for weather and climate and ensure these are built on an efficient, scalable and sustainable software infrastructure. The work requires technical expertise to create state-of-the-art and sustainable machine learning models but also to develop the required underlying software infrastructure. It necessitates excellent soft skills to work seamlessly in an international, interdisciplinary, and highly diverse working environment. The role will, in particular, support the scientific and technical development of a machine-learned Earth system model and contribute to Anemoi, the software library for machine learning-based weather forecasting and Earth system modelling developed by ECMWF and its member states. It will also contribute the maintenance of the WeatherGenerator code base that is used in the EarthGenerator project and help increase the HPC efficiency of the model. The position will also support the CLAIMA project, which develops infrastructure for machine learning climate models, and wider developments around the WeatherGenerator.
About The Team
The position will be located in the Machine Learning Modelling Team in the Earth System Modelling Section of the Research Department.
The Machine Learning Modelling Team focuses on the development and application of machine learning for Earth system modelling. This includes developments for ECMWF’s operational Artificial Intelligence Forecasting System (AIFS), machine learning models to represent Earth system components such as ocean, sea ice and land, and methodological developments such as foundation modelling and generative machine learning.
The advertised position will work in very close collaboration with physical modellers and several other teams and sections within ECMWF. It will also have a direct impact on 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 cuttingedge science and world-leading weather predictions and monitoring of the Earth system.
About The Project
This role is funded through two major European initiatives: Destination Earth (DestinE) and EarthGenerator.
Destination Earth, an initiative of the European Commission, is developing Digital Twins of the Earth and a machine-learned Earth system model for weather and climate applications. This includes learned ocean, wave, sea-ice, land surface and hydrology models developed using Europe's largest EuroHPC supercomputers.
The WeatherGenerator Horizon Europe project builds a European foundation model for weather and climate. The EarthGenerator Horizon Europe project extends the concept of the WeatherGenerator and explores foundation models for Earth sciences that can be applied to long prediction timescales from multiple years to decades.
The CLAIMA Horizon Europe project will compare different machine learning models for use cases in the area of climate modelling.
Your Responsibilities
You will:
You will bring:
If you feel that you have the motivation and many of the skills for this role but don't meet precisely all of the criteria listed 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 Warning 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.
About Destination Earth (DestinE)
ECMWF is one of the entities entrusted with implementing the European Commission's DestinE initiative, alongside ESA and EUMETSAT, and works closely with over 100 partner institutions across Europe. DestinE delivers high-resolution, configurable digital twins of the Earth system that simulate past, present and plausible future environmental conditions and enable “what-if” scenario exploration.
ECMWF is responsible for the delivery of these digital twins and of the Digital Twin engine, the software infrastructure needed to power the digital twins on the European HPC Joint Undertaking (EuroHPC) and ECMWF’s supercomputers and to handle and enable access to their data via the DestinE infrastructure, as well as for a range of Artificial Intelligence/ Machine Learning (AI/ML) activities. These include developments towards an ML Earth system model and a range of AI solutions that increase system interactivity and usability of digital twin data.
The third phase of DestinE (June 2026 June 2028) marks the transition of the Climate Change Adaptation and Weather-induced Extremes Digital Twins, together with the Digital Twin Engine, into sustained operations. During this phase, the focus will be on consolidating, operating, and further evolving these key elements, as well as on maturing the AI Earth-system model components and AI-based solutions developed in phase 2.
The Climate DT is implemented by a partnership led by CSC, currently involving 12 leading climate institutions, supercomputing centres, national meteorological services, academia and industrial partners, through a contract procured by ECMWF.
For more about DestinE and the Climate DT, please seeand.
Other Information
Grade remuneration: The successful candidates will be recruited according to the scales of the Co-ordinated Organisations. In addition to basic salary, ECMWF also offers an attractive benefits package. Information about working with us and full details of salary scales and allowances are available on the ECMWF website at: .
Starting date: As soon as possible
Location: Reading, UK or Bonn, Germany (Candidates are expected to relocate to the duty station)
Hybrid working: As a multi-site organisation, ECMWF has adopted a hybrid organisation model which allows flexibility to staff to mix office working and teleworking. We allow for remote work 10 days/month away from the office, including up to 80 days/year away from the duty station country (within member states and co-operating states).
Interviews by videoconference (MS Teams) interviews will be conducted in English and are expected to take place approximately a month after the closing date.
Successful applicants and members of their family forming part of their households will be exempt from immigration restrictions.
If you require any special accommodations in order to participate fully in our recruitment process, please let us know. To contact the ECMWF Recruitment Team, please email jobs@ecmwf.int.
Who Can Apply
Applicants are invited to complete the online application form by clicking on theapplybutton 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 Cooperating States, Listed Below, As Well As From All EU Member States
ECMWF Member and Co-operating States are: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Hungary, Germany, Georgia, Greece, 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.
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.
We are looking for a highly motivated Machine Learning Scientist to help develop world-leading machine learning models for weather and climate and ensure these are built on an efficient, scalable and sustainable software infrastructure. The work requires technical expertise to create state-of-the-art and sustainable machine learning models but also to develop the required underlying software infrastructure. It necessitates excellent soft skills to work seamlessly in an international, interdisciplinary, and highly diverse working environment. The role will, in particular, support the scientific and technical development of a machine-learned Earth system model and contribute to Anemoi, the software library for machine learning-based weather forecasting and Earth system modelling developed by ECMWF and its member states. It will also contribute the maintenance of the WeatherGenerator code base that is used in the EarthGenerator project and help increase the HPC efficiency of the model. The position will also support the CLAIMA project, which develops infrastructure for machine learning climate models, and wider developments around the WeatherGenerator.
About The Team
The position will be located in the Machine Learning Modelling Team in the Earth System Modelling Section of the Research Department.
The Machine Learning Modelling Team focuses on the development and application of machine learning for Earth system modelling. This includes developments for ECMWF’s operational Artificial Intelligence Forecasting System (AIFS), machine learning models to represent Earth system components such as ocean, sea ice and land, and methodological developments such as foundation modelling and generative machine learning.
The advertised position will work in very close collaboration with physical modellers and several other teams and sections within ECMWF. It will also have a direct impact on 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 cuttingedge science and world-leading weather predictions and monitoring of the Earth system.
About The Project
This role is funded through two major European initiatives: Destination Earth (DestinE) and EarthGenerator.
Destination Earth, an initiative of the European Commission, is developing Digital Twins of the Earth and a machine-learned Earth system model for weather and climate applications. This includes learned ocean, wave, sea-ice, land surface and hydrology models developed using Europe's largest EuroHPC supercomputers.
The WeatherGenerator Horizon Europe project builds a European foundation model for weather and climate. The EarthGenerator Horizon Europe project extends the concept of the WeatherGenerator and explores foundation models for Earth sciences that can be applied to long prediction timescales from multiple years to decades.
The CLAIMA Horizon Europe project will compare different machine learning models for use cases in the area of climate modelling.
Your Responsibilities
You will:
- Contribute to an efficient and sustainable software infrastructure for world-leading machine learning models at ECMWF and the ECMWF Member and Co-operating States
- Support the evolution of Anemoi while ensuring that the code meets the highest standards and is long-term sustainable
- Support maintenance and improvements to the WeatherGenerator code base, ensuring its long-term viability
- Explore new machine learning architectures and capabilities for Earth system modelling for operational weather predictions and Earth system modelling more generally
- Implement and evaluate new models and training protocols for weather forecasting and Earth system modelling in the context of both AIFS and the WeatherGenerator
You will bring:
- 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
- Ability to work on and contribute effectively to large software projects following modern coding practices, including writing tests and reviewing code
- Experience with the development and evaluation of machine learning models, including model design, implementation, training, and scaling
- Experience with the application of machine learning in the wider context of Earth system modelling would be an advantage
- Experience in designing for and optimizing performance of machine learning tools for training and inference on single GPUs and large HPCs would be an advantage
- 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
- Experience in HPC or large data science projects is desirable
If you feel that you have the motivation and many of the skills for this role but don't meet precisely all of the criteria listed 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 Warning 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.
About Destination Earth (DestinE)
ECMWF is one of the entities entrusted with implementing the European Commission's DestinE initiative, alongside ESA and EUMETSAT, and works closely with over 100 partner institutions across Europe. DestinE delivers high-resolution, configurable digital twins of the Earth system that simulate past, present and plausible future environmental conditions and enable “what-if” scenario exploration.
ECMWF is responsible for the delivery of these digital twins and of the Digital Twin engine, the software infrastructure needed to power the digital twins on the European HPC Joint Undertaking (EuroHPC) and ECMWF’s supercomputers and to handle and enable access to their data via the DestinE infrastructure, as well as for a range of Artificial Intelligence/ Machine Learning (AI/ML) activities. These include developments towards an ML Earth system model and a range of AI solutions that increase system interactivity and usability of digital twin data.
The third phase of DestinE (June 2026 June 2028) marks the transition of the Climate Change Adaptation and Weather-induced Extremes Digital Twins, together with the Digital Twin Engine, into sustained operations. During this phase, the focus will be on consolidating, operating, and further evolving these key elements, as well as on maturing the AI Earth-system model components and AI-based solutions developed in phase 2.
The Climate DT is implemented by a partnership led by CSC, currently involving 12 leading climate institutions, supercomputing centres, national meteorological services, academia and industrial partners, through a contract procured by ECMWF.
For more about DestinE and the Climate DT, please seeand.
Other Information
Grade remuneration: The successful candidates will be recruited according to the scales of the Co-ordinated Organisations. In addition to basic salary, ECMWF also offers an attractive benefits package. Information about working with us and full details of salary scales and allowances are available on the ECMWF website at: .
Starting date: As soon as possible
Location: Reading, UK or Bonn, Germany (Candidates are expected to relocate to the duty station)
Hybrid working: As a multi-site organisation, ECMWF has adopted a hybrid organisation model which allows flexibility to staff to mix office working and teleworking. We allow for remote work 10 days/month away from the office, including up to 80 days/year away from the duty station country (within member states and co-operating states).
Interviews by videoconference (MS Teams) interviews will be conducted in English and are expected to take place approximately a month after the closing date.
Successful applicants and members of their family forming part of their households will be exempt from immigration restrictions.
If you require any special accommodations in order to participate fully in our recruitment process, please let us know. To contact the ECMWF Recruitment Team, please email jobs@ecmwf.int.
Who Can Apply
Applicants are invited to complete the online application form by clicking on theapplybutton 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 Cooperating States, Listed Below, As Well As From All EU Member States
ECMWF Member and Co-operating States are: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Hungary, Germany, Georgia, Greece, 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.
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.
