Geospatial AI Platform Lead for Foundation Models

ETH Zürich; Root; Vor Ort; CHF 180.000 - 240.000; Vollzeit

Benefits dieser Stelle

Senior leadership

Impactful work

Close to ETH Zurich

Root/Lucerne location

Innovation ecosystem

Zusammenfassung

ETH Zürich is establishing the ETH Swiss GeoLab, a new interdisciplinary unit under ETH Space, located in Root near Lucerne. The GeoLab focuses on Earth observation, AI analytics and data platforms, shaping geospatial capabilities with societal impact.

You will lead a cross‑functional team, setting the technical strategy and architecture for multimodal foundation models and data pipelines. You will work with academy and industry partners to deliver scalable, reusable solutions, ensuring delivery

Qualifikationen

• Master’s or PhD in computer science, machine learning, geoinformatics, engineering or related field.
• Several years leading technical AI/ML, geospatial data or large‑scale software projects with cross‑functional teams.
• Experience defining technical architectures and guiding interdisciplinary teams.
• Strong expertise in machine learning, multimodal foundation models and agentic AI concepts.
• Excellent written and spoken English; German or French is a plus.

Aufgaben

• Define and implement ETH Swiss GeoLab’s geospatial AI and technical strategy.
• Establish the target architecture for multimodal models, data pipelines and shared platform.
• Lead cross‑functional teams and manage resources and priorities within the charter and budget.
• Drive development, evaluation and operationalisation of foundation models and agentic capabilities.
• Coordinate with CSCS, ETH units, partners to ensure scalability, security and maintainability.

Jobbeschreibung

ETH Zürich is establishing the ETH Swiss GeoLab, a new interdisciplinary unit under ETH Space, located in Root near Lucerne. The GeoLab focuses on Earth observation, AI analytics and data platforms, shaping geospatial capabilities with societal impact.

You will lead a cross‑functional team, setting the technical strategy and architecture for multimodal foundation models and data pipelines. You will work with academy and industry partners to deliver scalable, reusable solutions, ensuring delivery