Descripción de la oferta
Job Reference: 421_26_ES_CES_RE2 Position: Data/ML Engineer for Regional AI Weather/Climate Forecasting Datasets (RE1-2) Closing Date: Thursday, 22 October, **** Reference: 421_26_ES_CES_RE2 Job title: Data/ML Engineer for Regional AI Weather/Climate Forecasting Datasets (RE1-2) About BSC The Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS) is the leading supercomputing center in Spain. 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. 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. The role involves close collaboration with an international consortium of European meteorological and research institutions. Key Duties Curate and assess regional analyses, forecasts, and reanalyses for training and evaluating the regional AI forecasting system Design, build, and maintain reproducible ML data pipelines that transform raw simulation and observational data into Anemoi-compatible datasets Implement automated testing and CI/CD pipelines for the data-processing workflows Support deployment and validation of data pipelines on HPC and AI Factory infrastructure, including scalability and performance testing Collaborate with the international consortium and document pipelines to ensure reproducibility and traceability Requirements Education Bachelor's degree in Computer Science, Physics, Environmental/Atmospheric Science, or a related discipline. A Master's degree or PhD in a relevant field will be highly valued. Essential Knowledge and Professional Experience 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 Additional Knowledge and Professional Experience Familiarity with the Anemoi ecosystem (anemoi-datasets, anemoi-training) or similar ML-ready NWP dataset frameworks will be highly valued. 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. Fluency in English is essential. Proficiency in Spanish and other European languages would be advantageous. Competences Ability to work independently and within an international, multi-partner consortium to achieve project goals. Strong problem-solving skills and attention to detail. Excellent communication skills to collaborate effectively with interdisciplinary teams. Conditions The position will be located at BSC within the Earth Sciences Department. We offer a full-time contract (35h/week), a good working environment, a highly stimulating environment with state-of-the-art infrastructure, flexible working hours, extensive training plan, restaurant tickets, private health insurance, support to the relocation procedures. Duration: Open-ended contract due to technical and scientific activities linked to the project and budget duration. Holidays: 22 days of holidays + 6 personal days + 24th and 31st of December per our collective agreement. Salary: we offer a competitive salary commensurate with the qualifications and experience of the candidate and according to the cost of living in Barcelona. Starting date: As soon as possible. Development of the recruitment process The selection will be carried out through a competitive examination system ('Concurso-Oposición'). The recruitment process consists of two phases: Curriculum Analysis: Evaluation of previous experience and/or scientific history, degree, training, and other professional information relevant to the position. 40 points Interview phase: The highest-rated candidates at the curriculum level will be invited to the interview phase, conducted by the corresponding department and Human Resources. 60 points. A minimum of 30 points out of 60 must be obtained to be eligible for the position. The recruitment panel will be composed of at least three people, ensuring at least 25% representation of women. In accordance with OTM-R principles, a gender-balanced recruitment panel is formed for each vacancy at the beginning of the process. After reviewing the content of the applications, the panel will begin the interviews, with at least one technical and one administrative interview. At a minimum, a personality questionnaire as well as a technical exercise will be conducted during the process. The panel will make a final decision, and all individuals who participated in the interview phase will receive feedback with details on the acceptance or rejection of their profile. At BSC, we seek continuous improvement in our recruitment processes. For any suggestions or comments/complaints about our recruitment processes, please contact ****** more information, please follow this link. Deadline The vacancy will remain open until a suitable candidate has been hired. Applications will be regularly reviewed and potential candidates will contact. OTM-R principles for selection processes Equal opportunity and inclusion BSC-CNS is a committed equal opportunity employer. We are pleased to consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or any other basis protected by applicable state or local law. BSC-CNS is an equal opportunity employer committed to diversity and inclusion. #J-18808-Ljbffr
