Machine Learning Performance Engineer

Your role

We are searching for a highly motivated Machine Learning Performance Engineer to join the HPC Applications team  this role, you will have a particular focus on benchmarking and optimising machine learning (ML) models within the Destination Earth (Destin

E) initiative of the European Commission so that they run efficiently across a variety of EuroHPC systems such as LUMI, Leonardo, Mare Nostrum 5 and JUPITER as well as ECMWF’s own in-house HPC systems. Candidates are encouraged to apply even if they don’t possess any machine learning experience but have experience optimising scientific applications on large scale heterogeneous high‑performance computing systems.

At ECMWF, you will find a passionate community, collectively aiming to build world‑leading global Earth system models for weather prediction and climate simulations using both physics‑based and machine learning approaches. ECMWF has pioneered the AIFS as an operational data‑driven forecasting system, and played a key role in the development of Anemoi, an open‑source software framework for data‑driven earth system modelling. Within Destin

E, ECMWF develop earth‑system data‑driven model components, and oversee the development of a data‑driven climate emulator and data‑driven regional modelling system, each of which build the Anemoi framework.

The successful applicant’s work with the existing HPC Applications, Destin

E and ML teams will not only contribute to improving the computational performance and scalability of ML models on the world's largest supercomputers but also enable ECMWF to bring and optimise innovative ML approaches into the Destin

E Digital Twin Engine workflows, as well as in its operational prediction workflows. This effort supports delivering an AI Earth System Model in Destin

E, as well as ECMWF’s strategy of producing cutting‑edge science and world‑leading weather predictions and monitoring of the Earth system.

This position is based in the HPC Applications team, responsible for making sure that applications, such as the ECMWF IFS physics‑based model and the AIFS machine learning model, run as efficiently as possible on internal and external HPC systems and that they are able to scale across the world's largest supercomputers. The team is also responsible for developing benchmarks for procuring ECMWF's in‑house HPC systems used for operational weather forecasting and for providing application support for the operational weather forecasting suites.

European Centre for Medium‑Range Weather Forecasts (ECMWF) is a world leader in Numerical Weather Predictions providing high‑quality data for weather forecasts and environmental monitoring. As an intergovernmental organisation, we collaborate internationally to serve our members and the wider community with global weather predictions, data and training activities that are critical to contribute to safe and thriving societies.

The success of our activities depends on the funding and partnerships of the 35 Member and Co‑operating States who provide the support and direction of our work. Our talented staff together with the international scientific community, and our powerful supercomputing capabilities, are the core of a 24/7 research and operational centre with a focus on medium and long‑range predictions. We also hold one of the largest meteorological data archives in the world.

ECMWF has also developed a strong partnership with the European Union and has been entrusted with the implementation and operation of the Destination Earth Initiative and the Climate Change and Atmosphere Monitoring Services of the Copernicus Programme and the Strengthening Early Earning in Africa (SEWA) Programme. Other areas of work include High Performance Computing and the development of digital tools that enable ECMWF to extend provision of data and products covering weather, climate, air quality, fire and flood prediction and monitoring.

Our vision:
The strength of a common goal

Our mission:
Deliver global numerical weather predictions focusing on the medium‑range and monitoring of the Earth system to and with our Member States

ECMWF is a multi‑site organisation, with its…