ESA Graduate Trainee in Data and AI Infrastructure Engineering

Support the development of data-driven tools and AI applications for space exploration missions.

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ESA - European Space Agency

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Application deadline 4 months ago: Saturday 28 Feb 2026 at 23:59 UTC

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Overview

Support the development of data-driven tools and AI applications for space exploration missions.

You have:

  • Recently completed or currently in the final year of a master's degree in data science, computer science, artificial intelligence, or a related technical field.
  • Strong background in data engineering or machine learning, with programming experience and interest in AI infrastructure.
  • A solid understanding of machine learning concepts and practical experience applying them to projects.
  • Proficiency in Python, C++, or other languages relevant for data science and machine learning applications.
  • Familiarity with data preparation and analysis techniques, and experience with datasets from experiments or simulations.
  • Experience in developing prototypes or small-scale applications in AI or data processing.
  • Strong written and verbal communication skills to explain technical concepts to various audiences.
  • Ability to manage tasks independently, prioritise effectively, and work in a structured manner.
  • Experience working in international teams, considered an asset.
  • A willingness to learn and grow in data engineering, AI, and software infrastructure.

Location

EAC, Porz-Wahn, Germany.

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

Knowledge of relevant technical/functional domains

Relevant experience gained during internships, project work and/or extracurricular or other activities

General knowledge of the space sector and relevant activities

Knowledge of ESA and its programmes/projects

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.

Diversity, Equity and Inclusiveness ESA is an equal opportunity employer, committed to achieving diversity within the workforce and creating an inclusive working environment. We therefore welcome applications from all qualified candidates irrespective of gender, sexual orientation, ethnicity, religious beliefs, age, disability or other characteristics.

At the Agency we value diversity, and we welcome people with disabilities. Whenever possible, we seek to accommodate individuals with disabilities by providing the necessary support at the workplace. The Human Resources Department can also provide assistance during the recruitment process. If you would like to discuss this further, please contact us via email at [email protected].

Important Information and Disclaimer Applicants must be eligible to access information, technology, and hardware which is subject to European or US export control and sanctions regulations.

During the recruitment process, the Agency may request applicants to undergo selection tests. Additionally, successful candidates will need to undergo basic screening before appointment, which will be conducted by an external background screening service, in compliance with the European Space Agency's security procedures.

The information published on ESA’s careers website regarding working conditions is correct at the time of publication. It is not intended to be exhaustive and may not address all questions you would have.

Nationality and Languages Please note that applications can only be considered from nationals of one of the following States: Austria, Belgium, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, the Netherlands, Norway, Poland, Portugal, Romania, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom. Nationals from Latvia, Lithuania and Slovakia as Associate Member States, or Canada as a Cooperating State, can apply as well as those from Bulgaria, Croatia, Cyprus and Malta as European Cooperating States (ECS).

According to the ESA Convention, the recruitment of staff must take into account an adequate distribution of posts among nationals of the ESA Member States*. When short-listing for an interview, priority will be given to external candidates from under-represented Member States*.

The working languages of the Agency are English and French. A good knowledge of one of these is required. Knowledge of another Member State language would be an asset.

*Member States, Associate Members or Cooperating States.

Potential interview questions

Describe a project where you applied machine learning techniques. What challenges did you face? This question assesses your practical experience in machine learning and problem-solving skills. Discuss the project goals, techniques used, and any obstacles you encountered.
How do you ensure the quality and integrity of datasets you work with? This question evaluates your understanding of data preparation and processing standards. Pro members can see the explanation.
Tell us about a time you collaborated with a diverse team. What did you learn from that experience? Pro members can see the explanation. Pro members can see the explanation.
What programming languages and tools do you prefer for data science tasks and why? Pro members can see the explanation. Pro members can see the explanation.
Can you explain a complex technical concept to a non-technical audience? How did you approach it? Pro members can see the explanation. Pro members can see the explanation.
What strategies do you use to stay organized when managing multiple projects? Pro members can see the explanation. Pro members can see the explanation.
Describe a situation where you had to adapt to changing requirements in a project. How did you handle it? Pro members can see the explanation. Pro members can see the explanation.
What motivates you to work in the field of space exploration? Pro members can see the explanation. Pro members can see the explanation.
Added 4 months ago - Updated 4 months ago - Source: jobs.esa.int