Data/Ml Engineer For Regional Ai Weather/Climate Forecasting Datasets
Builds AI-ready datasets and reproducible ML data pipelines for regional AI weather/climate forecasting in the EU Destination Earth initiative, curating simulation and observational data and validating pipelines on HPC/GPU infrastructure. Core stack: Python, scientific data formats (NetCDF, GRIB, Zarr), Xarray, Slurm/GPU HPC, Anemoi, CI/CD.
- Compensation
- Not disclosed
- Location
- Barcelona, Spain
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Contract
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Bachelor's degree in Computer Science, Physics, Environmental/Atmospheric Science, or a related discipline
- Strong programming skills in Python and experience building data-processing/ETL pipelines
- Experience handling scientific data formats such as NetCDF, GRIB, Zarr, and tools such as Xarray, CDO/NCO
- Experience preparing datasets for machine-learning training pipelines, ideally in Earth, weather, or climate sciences
- Familiarity with UNIX/Linux environments and scripting languages
- Experience with high-performance computing (HPC) environments, including job schedulers (e.g. Slurm) and GPU-based workflows
- Fluency in English
- Ability to work independently and within an international, multi-partner consortium
- Strong problem-solving skills and attention to detail
- Excellent communication skills to collaborate effectively with interdisciplinary teams
Nice to have
- Master's degree or PhD in a relevant field
- Familiarity with the Anemoi ecosystem (anemoi-datasets, anemoi-training) or similar ML-ready NWP dataset frameworks
- Knowledge of NWP/reanalysis datasets (e.g. ERA5, CERRA, regional model output) and data-quality assessment
- Experience with CI/CD tools, containerization (Docker/Podman) and automated testing
- Experience with version control systems like Git
- Proficiency in Spanish and other European languages
Full description
Job Reference421_26_ES_CES_RE2PositionData/ML Engineer for Regional AI Weather/Climate Forecasting Datasets (RE1-2)Closing DateThursday, 22 October, Reference:421_26_ES_CES_RE2Job title:Data/ML Engineer for Regional AI Weather/Climate Forecasting Datasets (RE1-2)
It houses MareNostrum, one of the most powerful supercomputers in Europe, was a founding and hosting member of the former European HPC infrastructure PRACE (Partnership for Advanced Computing in Europe), and is now hosting entity for EuroHPC JU, the Joint Undertaking that leads large-scale investments and HPC provision in Europe.
The mission of BSC is to research, develop and manage information technologies in order to facilitate scientific progress.
BSC combines HPC service provision and R&D into both computer and computational science (life, earth and engineering sciences) under one roof, and currently has over staff from 60 countries.
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We are particularly interested for this role in the strengths and lived experiences of women and underrepresented groups to help us avoid perpetuating biases and oversights in science and IT research.
In instances of equal merit, the incorporation of the under-represented sex will be favoured.
We promote Equity, Diversity and Inclusion, fostering an environment where each and every one of us is appreciated for who we are, regardless of our differences.
If you consider that you do not meet all the requirements, we encourage you to continue applying for the job offer.
We value diversity of experiences and skills, and you could bring unique perspectives to our team.
Context And Mission
The successful candidate will join the Computational Earth Sciences group within the Earth Sciences department to contribute to projects within the Destination Earth (DestinE) initiative.
DestinE is a European Union flagship programme building high-precision digital replicas ("Digital Twins") of the Earth system to support climate adaptation and extreme-weather preparedness.
The candidate will work to develop AI-ready datasets and data pipelines that support the training and evaluation of regional AI weather and climate forecasting systems, built on open-source frameworks such as Anemoi.
This includes curating and preparing high-resolution simulation and observational data, building reproducible pipelines to make these datasets machine-learning-ready, and supporting their deployment and validation on high-performance and AI-oriented computing infrastructure.