Machine Learning Engineer (RE1-2) – AI Factory (Earth Sciences Department)
Barcelona Supercomputing Center (BSC)
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Résumé du poste
Barcelona Supercomputing Center (BSC) seeks a Machine Learning Engineer for the Earth Sciences department to enhance AI services for climate applications, manage software integration, and collaborate on research projects.
The role focuses on developing and deploying ML pipelines, coordinating AI software across consortium partners, and ensuring robust support for the AI Factory user community in a cutting-edge HPC environment in Barcelona.
Qualifications
- Bons conhecimentos de Python e bibliotecas de ML como PyTorch, TensorFlow e Scikit-learn.
- Experiência comprovada em desenvolver e treinar modelos de ML, especialmente deep learning.
- Forte capacidade de lidar, analisar e validar grandes conjuntos de dados.
- Experiência em ambiente computacional UNIX e HPC.
Responsabilités
- Support the documentation and curation of AI software employed in the AI Factory
- Support the deployment and maintenance of AI software on MareNostrum5 AI partition
- Design, implement, and optimize machine learning pipelines for environmental-related applications
- Collaborate with domain scientists and external users to develop AI solutions
- Support users from the AI Factory in accessing and utilizing AI tools and services
- Participate in collaborative development within the AI Factory consortium
- Participate in technical reporting and scientific publications contributing to the documentation of the AI Factory software
Connaissances
Formation
Outils
Description du poste
The Barcelona Supercomputing Center (BSC) is seeking a Machine Learning Engineer to join the Earth Sciences department within the framework of the AI Factory initiative.
The AI Factory is a major European project aimed at accelerating the adoption and development of artificial intelligence across industry sectors that is deploying a comprehensive set of AI-focused services, including robust training, networking, and innovation support structures. Its mission is to foster the uptake and effective use of AI, particularly among SMEs and startups across participating countries, and to strengthen the European innovation ecosystem. These services are powered by the AI-specific partition of the MareNostrum5 supercomputer, one of the most advanced infrastructures in Europe, designed to meet the evolving AI needs by harnessing the latest AI-oriented computing technologies.
Within the Earth Sciences department, the selected candidate will support the development and deployment of AI services related to climate change use and test cases, contributing to real-world applications and scientific advancements. Additionally, the selected candidate will be coordinating the availability and integration of AI software, developed internally by consortium members or externally by third parties, on the MareNostrum5 AI partition, ensuring smooth support for the wider AI Factory user community.
This project is funded by the European Union’s Recovery and Resilience Facility, in accordance with Council Regulation (EU) 2020/2094 of 14 December 2020, and regulated by Regulation (EU) 2021/241 of the European Parliament and of the Council of 12 February 2021, in the context of the “BSC AI Factory” project.
This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101234399 and Spain, Portugal, Romania and Türkiye. The JU receives support from the European Union’s Horizon Europe Programme. It is also co-funded by the Spanish Ministry for Digital Transformation and the Civil Service and counts with the support of the Government of Catalonia.
Key Duties
- Support the documentation and curation of AI software employed in the AI Factory
- Support the deployment and maintenance of AI software on MareNostrum5 AI partition
- Design, implement, and optimize machine learning pipelines for environmental-related applications.
- Collaborate with domain scientists and external users to develop AI solutions
- Support users from the AI Factory in accessing and utilizing AI tools and services
- Participate in collaborative development within the AI Factory consortium
- Participate in technical reporting and scientific publications contributing to the documentation of the AI Factory software, with opportunities to be involved in academic publications and project reporting.
Requirements
- EducationBachelor’s or Master’s in Computer Science, Machine Learning, Data Science, Environmental Sciences, or a related field
- Essential Knowledge and Professional ExperienceStrong programming skills in Python, with experience in machine learning libraries such as PyTorch, Tensorflow, and Scikit-learn
- Proven experience in developing and training machine learning models, particularly deep learning architectures
- Strong background in handling, analyzing, and validating large-scale datasets
- Experience working in a UNIX-based computational environment
- Additional Knowledge and Professional ExperienceFamiliarity with climate, weather, and Earth system datasets (NetCDF, Zarr)
- Experience in high-performance computing (HPC) and parallelized machine learning workflows
- Proficiency in GPU-accelerated machine learning frameworks such as TensorFlow, RAPIDS, JAX, and/ or distributed training using Dask
- Understanding of climate and weather models
- CompetencesStrong problem-solving and analytical skills, with the ability to optimize computational workflows
- Ability to work independently while collaborating effectively in a research environment
- Excellent communication skills, with a strong ability to document and present research findings
- Proficiency in written and spoken English
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
The Barcelona Supercomputing Center (BSC-CNS) is Spain's national supercomputing hub, advancing research in high-performance computing and fostering innovation across various scientific disciplines.
The Barcelona Supercomputing Center seeks a Machine Learning Engineer for the Earth Sciences department to enhance AI services for climate applications, manage software integration, and collaborate on research projects.
Full-time
Barcelona Supercomputing Center (BSC)
Barcelona, ES
The Barcelona Supercomputing Center seeks a Machine Learning Engineer for the Earth Sciences department to enhance AI services for climate applications, manage software integration, and collaborate on research projects.
Context And Mission
The Barcelona Supercomputing Center (BSC) is seeking a Machine Learning Engineer to join the Earth Sciences department within the framework of the AI Factory initiative.
The AI Factory is a major European project aimed at accelerating the adoption and development of artificial intelligence across industry sectors that is deploying a comprehensive set of AI-focused services, including robust training, networking, and innovation support structures. Its mission is to foster the uptake and effective use of AI, particularly among SMEs and startups across participating countries, and to strengthen the European innovation ecosystem. These services are powered by the AI-specific partition of the MareNostrum5 supercomputer, one of the most advanced infrastructures in Europe, designed to meet the evolving AI needs by harnessing the latest AI-oriented computing technologies.
Within the Earth Sciences department, the selected candidate will support the development and deployment of AI services related to climate change use and test cases, contributing to real-world applications and scientific advancements. Additionally, the selected candidate will be coordinating the availability and integration of AI software, developed internally by consortium members or externally by third parties, on the MareNostrum5 AI partition, ensuring smooth support for the wider AI Factory user community.
This project is funded by the European Union’s Recovery and Resilience Facility, in accordance with Council Regulation (EU) 2020/2094 of 14 December 2020, and regulated by Regulation (EU) 2021/241 of the European Parliament and of the Council of 12 February 2021, in the context of the “BSC AI Factory” project.
This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101234399 and Spain, Portugal, Romania and Türkiye. The JU receives support from the European Union’s Horizon Europe Programme. It is also co-funded by the Spanish Ministry for Digital Transformation and the Civil Service and counts with the support of the Government of Catalonia.
Key Duties
- Support the documentation and curation of AI software employed in the AI Factory
- Support the deployment and maintenance of AI software on MareNostrum5 AI partition
- Design, implement, and optimize machine learning pipelines for environmental-related applications.
- Collaborate with domain scientists and external users to develop AI solutions
- Support users from the AI Factory in accessing and utilizing AI tools and services
- Participate in collaborative development within the AI Factory consortium
- Participate in technical reporting and scientific publications contributing to the documentation of the AI Factory software, with opportunities to be involved in academic publications and project reporting.
Requirements
- EducationBachelor’s or Master’s in Computer Science, Machine Learning, Data Science, Environmental Sciences, or a related field
- Essential Knowledge and Professional ExperienceStrong programming skills in Python, with experience in machine learning libraries such as PyTorch, Tensorflow, and Scikit-learn
- Proven experience in developing and training machine learning models, particularly deep learning architectures
- Strong background in handling, analyzing, and validating large-scale datasets
- Experience working in a UNIX-based computational environment
- Additional Knowledge and Professional ExperienceFamiliarity with climate, weather, and Earth system datasets (NetCDF, Zarr)
- Experience in high-performance computing (HPC) and parallelized machine learning workflows
- Proficiency in GPU-accelerated machine learning frameworks such as TensorFlow, RAPIDS, JAX, and/ or distributed training using Dask
- Understanding of climate and weather models
- CompetencesStrong problem-solving and analytical skills, with the ability to optimize computational workflows
- Ability to work independently while collaborating effectively in a research environment
- Excellent communication skills, with a strong ability to document and present research findings
- Proficiency in written and spoken English
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
The HiPEAC project has received funding from the European Union's Horizon Europe research and innovation funding programme under grant agreement number 101296676. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
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