Select how often (in days) to receive an alert:

EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than 6,500 people supporting the three main missions of the institutions: education, research and innovation. The EPFL campus offers an exceptional working environment at the heart of a community of more than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries

Main duties and responsibilities

• Rebuild the RVI scoring engine as clean, standalone Python code, implementing the validated indicator formulas correctly
• Design and build the database layer (schema and storage) and connect it directly to the RVI engine
• Migrate the graph representation from Neo4j to a Python-native structure
• Implement data-ingestion and similarity-matching logic that completes missing component information (GNN)
• Build the web interface: data upload in, ranked results out
• Work independently from rough goals to working software, using modern tooling (including AI coding assistants) to move fast
• Keep the codebase clean and documented so it can be extended by others

Profile

• The role combines graph data, ML, and practical web development. Some technologies may be learned during the project, but solid Python skills and hands-on experience with graphs and GNNs are essential
• Full-stack: able to own backend, data layer and frontend as one coherent system
• Degree in computer science, software engineering, data science, machine learning, or an equivalent field;
• Strong Python skills; experience with APIs, databases, data pipelines, testing and deployment
• Has personally built and shipped at least one complete software product or system beyond coursework
• Able to translate research or mathematical logic into reliable software
• Uses AI coding tools effectively, but independently reviews, tests and takes full responsibility for the resulting code
• Graph data, ML/GNN, and LLM experience is a strong advantage
• Ability to parse and work with structured building-data formats (BIM/IFC), or equivalent experience ingesting structured technical data from PDF/Excel sources
• Civil engineering or broader construction-industry background is an advantage, not required
• Valid Swiss work permit is mandatory. Applications without one will not receive a response
• Priority will be given to candidates already based in Canton Vaud or the Lausanne region, available to start immediately

We offer

• The chance to build the technical core of an EPFL spin-out in formation, in circular construction and AI
• Direct collaboration with the founder and real ownership of what you build
• A flexible, hands-on role with genuine impact

Continued involvement possible if further REON funding is secured