Team Leader - Machine Learning Engineering in Reading

We are seeking an experienced machine learning engineer to lead the Machine Learning Engineering Team at the 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 operationalised 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 operationalising 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.

In this role you will:

• 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 ECMWF at events, towards Member States and beyond.

What we are looking for:

• 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 requirements of the role, with the ability to adapt to changing priorities.
• Curiosity and drive to explore new machine learning solutions, and capability to drive innovative ideas forward.
• Exceptional interpersonal and communication skills.

Your profile:

• Advanced university degree (EQ7 level or above) or equivalent professional experience in computer science or engineering, computational science, physics or natural sciences, mathematics, or a related discipline.
• Demonstrated experience in managing technical projects, including planning, prioritisation and coordination across multiple stakeholders.
• Demonstrated experience in managing others and leading diverse groups of people is highly desirable.
• Experience in machine learning workflows, including training and inference pipelines.
• Knowledge of Earth‑system modelling or data‑driven weather forecasting would be an advantage.
• Demonstrated experience developing object‑oriented software in Python.
• Experience contributing to large‑scale software projects, preferably open source related to machine learning and/or involving multiple software components.
• Experience dealing with users, gathering feedback and planning developments.
• Knowledge of model versioning, experiment tracking, and reproducibility.
• Experience with CI/CD pipelines and test‑driven development would be an advantage.

Candidates must be able to work effectively in English; knowledge of one of the Centre’s other working languages (French or German) is an advantage.

At ECMWF, we consider an inclusive environment as key for our success. We are dedicated to ensuring a workplace that embraces diversity and provides equal opportunities for all, without distinction as to race, gender, age, marital status, social status, disability, sexual orientation, religion, personality, ethnicity and culture.

Grade remuneration: The successful candidates will be recruited according to the scales of the Co‑ordinated Organisations. Details of salary scales and allowances are available on the ECMWF website at www.ecmwf.int/en/about/jobs.

Starting date: as soon as possible.