Team Leader - Machine Learning Engineering

Your role

We are seeking an experienced machine learning engineer to lead the Machine Learning Engineering Team at European Centre for Medium-Range Weather Forecasts (ECMWF). As Team Leader (A3), you will provide technical direction for a multidisciplinary group working at the forefront of machine learning for operational weather forecasting. You will guide the team’s priorities and development activities, coordinate collaboration across ECMWF and its Member States, and play a central role in project managing and shaping the evolution of the Anemoi framework.

Working closely with scientists, software engineers and operational teams, you will drive the delivery of robust, scalable ML systems that bring cutting‑edge machine learning into production forecasting environments.

As Team Leader, you will ensure the team has the direction, resources and support needed to deliver effectively. You will set priorities and targets, represent the team at internal and external events, and coordinate its activities with wider ECMWF initiatives. You will foster an environment where team members can propose ideas, raise issues and contribute to a culture of innovation across the Innovation Platform.

You will join a vibrant community committed to pushing the boundaries of numerical weather prediction through cutting‑edge technology and science. With recent breakthroughs in artificial intelligence and the rapid progress of AI‑driven weather forecasting, ECMWF is investing heavily in this area having ope rationalised data‑driven forecasting models, namely the Artificial Intelligence Forecasting System (AIFS). We have established a dedicated multidisciplinary group to ensure AIFS has real‑world impact, meeting the requirements of users and adding value in forecasting extreme events in a changing climate.

Together with our Member States, we are co‑developing Anemoi, an end‑to‑end framework for training and ope rationalising data‑driven weather forecasting models. AIFS is one example of what this system can produce, enabling meteorological organisations to combine data sources and training recipes to build their own forecasting models. Anemoi is being used in Europe and across the globe to develop operational weather forecasting models.

The role involves occasional travel (around 2‑5 missions per year), mainly to ECMWF's other duty stations or within our Member States and Co‑operating States.

Your role is central to shaping ECMWF’s contributions to Anemoi. You will coordinate and oversee development activities, work closely with scientists and engineers across ECMWF and its Member States. Ensuring that software is robust, scalable and ready for operational use will be a key part of your mission, as will engaging with the open source community to enhance onboarding, usability and maintainability.

You will contribute to the governance process of Anemoi. Working with colleagues across the Centre you will contribute to evolving the ways of working to ensure Anemoi continues to thrive in a dynamic setting.

• Act as Team Leader for the Machine Learning Engineering Team, including planning and prioritisation of the team’s work and enabling team development.
• Play a key role in overseeing the evolution of Anemoi, engaging across ECMWF and Member States, including contributing to Anemoi governance.
• Contribute to the strategic planning of ML activities across the Centre.
• Deliver, as an individual and as a team, innovative machine learning engineering solutions for the Centre.
• Represent the Machine Learning Engineering Team and ECWMF at events, towards Member States and beyond.

• Highly organised with the capacity to work on a diverse range of tasks to tight deadlines
• Passion for guiding, coaching and mentoring staff within the team
• Excellent analytical and problem‑solving skills with a proactive and constructive approach
• Ability and desire to take a leadership role within a team of subject matter experts
• Demonstrated previous experience of working well and building relationships within a team of scientific professionals and wider teams within an organisation
• Flexibility in handling the diverse…