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Scientist/Senior Scientist for Machine Learning (multiple vacancies)

Multiple locations

  • Organization: ECMWF - European Centre for Medium-Range Weather Forecasts
  • Location: Multiple locations
  • Grade: Junior level - A2 - Grade band
  • Occupational Groups:
    • Education, Learning and Training
    • Information Technology and Computer Science
    • Scientist and Researcher
    • Innovations for Sustainable Development
  • Closing Date: Closed

Job reference: VN23-14

Location: Flexible - Any ECMWF Duty Station

Deadline for applications: 10/04/2023

Publication date: 10/03/2023

Salary and Grade: Grade A3 EUR 87344 or GBP 84368, Grade A2 EUR 70794 or GBP 68374 NET of tax annual basic salary + other benefits

Contract type: STF-C / STF-PL

Department: Research or Forecast

Contract Duration: 2 to 4 years

Machine learning for weather and climate modelling is moving at a breath-taking pace. We are looking for exceptional individuals to help us propel our machine learning activities towards and beyond the goals of ECMWF’s machine learning roadmap that was published in 2021 ( and into operational numerical weather predictions and climate services. Specifically, this is your opportunity to explore large scale machine learning applications that can train from many tera-byte of training data, as multi-node multi-GPU applications, to enhance prediction capabilities at ECMWF. Approaches include blending conventional and deep learning tools to find optimal, hybrid solutions or develop prediction capabilities based on pure machine learning tools. 

At ECMWF, machine learning is used and investigated in many different application areas across the workflow of numerical weather predictions and climate services and forms an important part of the Destination Earth project as well as of the Copernicus Services. Our aim is to strengthen ECMWF’s leading position in Earth system modelling, by consolidating and enhancing the use of sophisticated machine learning tools as a crucial component in achieving ECMWF’s common goal. 

ECMWF is the European Centre for Medium-Range Weather Forecasts. It is an intergovernmental organisation created in 1975 by a group of European nations and is today supported by 35 Member and Co-operating States, mostly in Europe. The Centre’s mission is to serve and support its Member and Co-operating States and the wider community by developing and providing world-leading global numerical weather prediction. ECMWF functions as a 24/7 research and operational centre with a focus on medium and long-range predictions and holds one of the largest meteorological archives in the world. The success of its activities relies primarily on the talent of its scientists, strong partnerships with its Member and Co-operating States and the international community, some of the most powerful supercomputers in the world, and the use of innovative technologies such as machine learning across its operations. 

For additional details, see  

We are looking for exceptional individuals to join our machine learning venture.  We will discuss the exact positioning of the advertised roles within ECMWF, as well as the grading and specific responsibilities with you during the selection process as these will depend on your background and aspiration.  

You will be part of a team working closely and collaboratively with domain and machine learning scientists and computational engineers to improve one of the leading global weather prediction systems in the world. This will be complemented with expertise from the technology providers to ensure holistic understanding of the entire technology stack. 

Three of the positions will be responsible for developing large-scale machine learning solutions that will be able to process and train from very large datasets, and capable to train and infer as an efficient and scalable supercomputing application. The development will happen in close collaboration with domain experts and aligned with the current machine learning efforts.  

The fourth position will contribute to the MAELSTROM ( and RODEO projects, funded by EuroHPC-Joint Undertaking and Horizon Europe respectively. MAELSTROM is developing machine learning benchmark applications, software tools and compute hardware designs that are optimal for applications in the domain of weather and climate science. The RODEO project will provide open access to meteorological data via harmonised APIs and a common data catalogue to support the development of information products and services and AI applications. 

The following roles will be considered and can be tailored depending on your expertise: 

  • A coordinating role to oversee and guide several machine learning projects and developments across ECMWF, to represent ECMWF at conferences and committees, and to formulate a vision for future machine learning developments. 

  • A role as machine learning tool developer to generate, maintain and publish new datasets for machine learning and verification, to facilitate data management on various computing environments including supercomputers, and to develop new cutting-edge machine learning solutions for various application areas in numerical weather predictions.

  • A role as high-performance computing engineer for deep learning applications to make training and inference of large machine learning solutions scalable and efficient; to optimise data-parallel multi-node multi-GPU training; to tune data pipelines for efficient deployment in a production high-performance computing environment.  
  • Flexibility with a can-do attitude to handle the diverse requirements of this role
  • Excellent interpersonal and communication skills (verbal and written) 
  • Strong analytical and problem-solving skills, with a proactive approach 
  • A high degree of self-motivation, and the ability to work with minimal supervision 
  • Being a strong team player, maintaining effective communication and documentation
  • Ability to work efficiently on diverse tasks in a timely manner 

You can demonstrate experience in several of the following areas: 

  • Experience with the coordination of projects in machine learning  
  • Experience in the use of large deep learning applications in Earth system science 
  • Experience with the handling and publishing of multi-terabyte datasets for machine learning 
  • Experience with state-of-the-art deep learning applications such as graph networks, transformer networks or diffusion networks 
  • Experience with parallel training of machine learning applications in supercomputing environments incl High-Performance Computing 

You have experience and knowledge in the following areas: 

  • Knowledge of deep learning frameworks (e.g. pytorch, tensorflow) 
  • Knowledge and experience with python (pandas, xarray) 
  • Ability to train large deep learning tools on supercomputing hardware (traditional HPC and/or Cloud) and distributed data-parallel training 
  • Understanding of the main environments (programming, software and hardware) for deep learning 
  • Performance engineering and the optimisation of data loading 
  • Version control, release management and continuous integration of software stacks (such as git and pip) 

Candidates must be able to work effectively in English and interviews will be conducted in English. A good knowledge of one of the Centre’s other working languages (French or German) would be an advantage.

Grade remuneration:  The successful candidates will be recruited at the A2 or A3 grade, according to the scales of the Co-ordinated Organisations. The annual basic salary will be Grade A3 EUR 87,344 (Bologna) EUR 103,517 (Bonn) or GBP 84,368 (Reading), Grade A2 EUR 70,794 (Bologna), EUR 83,888 (Bonn) or GBP 68,374 (Reading) NET of income tax. ECMWF also offers a generous benefits package, including a flexible teleworking policy. The position will either be assigned to the employment category STF-C or STF-PL as defined in the ECMWF Staff Regulations. Full details of salary scales and allowances available on the ECMWF website at, including the ECMWF Staff Regulations and the terms and conditions of employment. 

Starting date:              As soon as possible

Length of contract:   2 to 4 years with the possibility of extensions, subject to funding.

Location:                      Bonn, Germany ; Bologna, Italy or Reading, UK

As a multi-site organisation, ECMWF has adopted a hybrid organisation model which allows flexibility to staff to mix office and tele-working. 

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

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

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 from all EU Member States. 

ECMWF Member 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. 

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. 


This vacancy is now closed.
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