Roles
We are looking to fill three positions: Machine Learning Scientist (Modeling team), Machine Learning Engineer (Engineering team), and Machine Learning Scientist (Modeling team). These roles include responsibilities for developing machine‑learning models, workflows, and infrastructure for weather and climate prediction and support the development of a machine‑learned Earth system model.
Your Responsibilities
- Build an efficient, sustainable software infrastructure for machine learning at ECMWF.
- Explore new machine‑learning architectures and capabilities for Earth system modelling for operational weather predictions and climate modelling.
Teams
The roles will be located across the Machine Learning Modelling Team in the Earth System Modelling Section and the Machine Learning Engineering Team in the Innovation Platform of the Forecast and Services Department.
What We Are Looking For (Across all roles)
- Excellent analytical and problem‑solving skills with a proactive approach.
- Strong interpersonal and communication skills to collaborate effectively with interdisciplinary teams.
- Self‑motivated, able to work with minimal supervision, and committed to teamwork.
- Ability to maintain clear documentation of scientific results.
- Highly organised and able to manage diverse tasks to tight deadlines.
- Experience with standard software development tools (e.g., git) and well‑structured, maintainable software.
- Experience contributing to large software projects following modern coding practices, including writing tests and reviewing code.
Specific Requirements by Role
Role A – Machine Learning Scientist (Modeling team)
- Experience developing and evaluating machine‑learning models, including model design, implementation, training, and scaling.
- Experience with machine‑learning models for Earth system science across time ranges from months to years.
- Experience developing models for sub‑seasonal, seasonal, or climate simulations is 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 in HPC or large‑scale GPU computing environments (e.g., NVIDIA DGX, EuroHPC) and job schedulers such as SLURM.
- Knowledge of large‑scale dataset processing and integration is 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 (Modeling team)
- Experience developing and evaluating machine‑learning models, including design, implementation, training, and scaling.
- Experience designing and optimizing performance of machine‑learning tools for training and inference.
- Practical knowledge of scaling models to large HPCs with hundreds or thousands of nodes is desirable.
- Experience applying machine learning in the wider context of Earth system modelling is an advantage.
Your Profile
- Advanced university degree (EQ7 or above) in a relevant field 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.
- Proficiency in English.
Benefits
- Grade remuneration according to the Co‑ordinated Organisations scale. Salary and allowances details are available on the ECMWF website.
- Starting date: as soon as possible.
- Relocation to Bonn, Germany, or Reading, UK, with a hybrid working model. Teleworking is allowed within the area of member and co‑operating states.
Inclusivity Statement
ECMWF values diversity and inclusivity. All applicants are considered equally, regardless of age, race, gender, sexual orientation, religion, disability, or other protected characteristics. Applicants from European Union member states, co‑operating states, and Ukrainian nationals are welcome.
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We are looking to fill three positions: Machine Learning Scientist (Modeling team), Machine Learning Engineer (Engineering team), and Machine Learning Scientist (Modeling team). These roles include responsibilities for developing machine‑learning models, workflows, and infrastructure for weather and climate prediction and support the development of a machine‑learned Earth system model.
Your Responsibilities
- Build an efficient, sustainable software infrastructure for machine learning at ECMWF.
- Explore new machine‑learning architectures and capabilities for Earth system modelling for operational weather predictions and climate modelling.
Teams
The roles will be located across the Machine Learning Modelling Team in the Earth System Modelling Section and the Machine Learning Engineering Team in the Innovation Platform of the Forecast and Services Department.
What We Are Looking For (Across all roles)
- Excellent analytical and problem‑solving skills with a proactive approach.
- Strong interpersonal and communication skills to collaborate effectively with interdisciplinary teams.
- Self‑motivated, able to work with minimal supervision, and committed to teamwork.
- Ability to maintain clear documentation of scientific results.
- Highly organised and able to manage diverse tasks to tight deadlines.
- Experience with standard software development tools (e.g., git) and well‑structured, maintainable software.
- Experience contributing to large software projects following modern coding practices, including writing tests and reviewing code.
Specific Requirements by Role
Role A – Machine Learning Scientist (Modeling team)
- Experience developing and evaluating machine‑learning models, including model design, implementation, training, and scaling.
- Experience with machine‑learning models for Earth system science across time ranges from months to years.
- Experience developing models for sub‑seasonal, seasonal, or climate simulations is 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 in HPC or large‑scale GPU computing environments (e.g., NVIDIA DGX, EuroHPC) and job schedulers such as SLURM.
- Knowledge of large‑scale dataset processing and integration is 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 (Modeling team)
- Experience developing and evaluating machine‑learning models, including design, implementation, training, and scaling.
- Experience designing and optimizing performance of machine‑learning tools for training and inference.
- Practical knowledge of scaling models to large HPCs with hundreds or thousands of nodes is desirable.
- Experience applying machine learning in the wider context of Earth system modelling is an advantage.
Your Profile
- Advanced university degree (EQ7 or above) in a relevant field 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.
- Proficiency in English.
Benefits
- Grade remuneration according to the Co‑ordinated Organisations scale. Salary and allowances details are available on the ECMWF website.
- Starting date: as soon as possible.
- Relocation to Bonn, Germany, or Reading, UK, with a hybrid working model. Teleworking is allowed within the area of member and co‑operating states.
Inclusivity Statement
ECMWF values diversity and inclusivity. All applicants are considered equally, regardless of age, race, gender, sexual orientation, religion, disability, or other protected characteristics. Applicants from European Union member states, co‑operating states, and Ukrainian nationals are welcome.
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