ESA Graduate Trainee in Data and AI Infrastructure Engineering

Our team and mission

The Software and Artificial Intelligence Team drives innovation in HRE projects and initiatives across diverse exploration destinations such as LEO, the Moon, Mars, and Earth analogues. As a central hub for software innovation, the team collaborates with internal and external tech experts to highlight the crucial role of advanced software in exploration. The team is structured around four core pillars: AI, XR, IoT, and Quantum technologies, with data serving as a foundational element throughout. This comprehensive approach ensures the effective integration of state-of-the-art technologies, positioning the team at the forefront of exploration advancements.

You can find out more about the work we do in AI here: https://blogs.esa.int/exploration/the-power-of-ai-in-space-exploration/ 

You also are encouraged to visit the ESA website: http://www.esa.int

Field(s) of activity/research for the traineeship

You will be working with the Software and AI Team to support the development of data-driven tools and AI-enabled applications for space exploration missions. In this role, you will be:

• Contributing to the design and implementation of data workflows, including the preparation, processing, and organisation of datasets used in simulations, analysis, and AI applications;
• Supporting the development and operation of AI workflows, helping run training and inference tasks on GPU-based systems, evaluating model performance, and assisting with the integration of modern AI tools and foundation models into prototypes;
• Helping expand and maintain software and AI infrastructure, including GPU servers, containerised environments, and CI/CD pipelines used to develop, test, and deploy data processing and AI applications;
• Assisting in the creation of automated processing pipelines, such as scripts and services that streamline data preparation, experiment execution, or model deployment;
• Preparing technical summaries, visualisations, and analytical reports to support internal reviews, decision-making, and project documentation;
• Collaborating with cross-functional teams to ensure consistent data standards, software engineering practices, and workflow integration across our Digital Twin, XR, and Exploration programmes.

Technical competencies

Behavioural competencies

Result Orientation

Operational Efficiency

Fostering Cooperation

Relationship Management

Continuous Improvement

Forward Thinking

For more information, please refer to ESA Core Behavioural Competencies guidebook

Education

You should have just completed, or be in the final year of your master’ s degree in data science, computer science, artificial intelligence, or a related technical field. 

Additional requirements

You should have good interpersonal and communication skills and should be able to work in a multicultural environment, both independently and as part of a team. Previous experience of working in international teams can be considered an asset. Your motivation, overall professional perspective and career goals will also be explored during the later stages of the selection process. 

You should bring a strong background in data engineering or machine learning, together with practical programming experience and an interest in AI infrastructure, GPU computing, and modern software development practices.

You should also have:

• AI and Machine Learning: A solid understanding of machine learning concepts, frameworks, and techniques, with some practical experience in applying them to real projects. Candidates should have hands-on experience and be able to demonstrate an understanding of why specific methods are used and how to implement them effectively in practice;
• Programming Knowledge: Proficiency in Python, C++, or other languages used for data science and machine learning applications. Experience with platforms such as NVIDIA Isaac, ROS is an asset;
• Data Processing & Workflow Skills: Familiarity with data preparation, transformation, and analysis techniques. Experience working with datasets from experiments, simulations, or similar technical environments is beneficial;
• Prototyping and Development: Experience in developing prototypes or small-scale applications, preferably in the context of AI, data processing, or automation. Ability to translate problem statements into practical technical solutions;
• Communication Skills: Strong written and verbal communication skills, with the ability to explain technical concepts clearly to both technical and non-technical audiences. Ability to document workflows, decisions, and analyses in a structured way;
• Self-Organisation: Ability to manage tasks independently, prioritise effectively, and work in a structured manner. Proactive in identifying issues and proposing solutions;
• Collaboration and Teamwork: Ability to work effectively in a multidisciplinary and multicultural team, contributing to shared objectives and supporting knowledge exchange across ESA departments;
• Learning Mindset: A strong motivation to learn and grow within the fields of data engineering, AI, and software infrastructure. Willingness to explore new tools, methods, and technologies relevant to space exploration;
• Problem-Solving: A practical, solution-oriented approach to technical challenges, with the ability to apply appropriate data or AI techniques while considering constraints and operational requirements.