The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character.
The Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services.
We play an instrumental role in Europe by fueling innovation through research partnerships with industry, boosting R&D investments leading to economic growth, and attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services & Applications.

The University of Luxembourg is seeking a passionate and entrepreneurial Research Engineer to join a cutting-edge applied research initiative funded by the FNR JUMP programme at the SnT research centre and to join SEDAN group (https://www.uni.lu/snt-en/research-groups/sedan). The successful candidate will work closely with the project lead and team to explore and commercialize innovations at the intersection of AI systems and machine learning, with a goal to spin out a deep-tech startup.
This is a unique opportunity for someone with both technical expertise and startup ambitions to be part of a venture creation process grounded in world-class research. You will play a central role in building and scaling ML-based systems using modern AI technologies and frameworks.
Moreover, this role is a unique chance to work on transformative AI technologies with the support of SnT’s leading infrastructure and ecosystem, to have a close collaboration with experienced researchers and entrepreneurs and it is also the opportunity to co-found a startup and play a leadership role in its technical and business development with access to state-of-the-art compute resources, funding, and mentorship

Your Responsibilities:

• Lead the design, development, and optimization of scalable AI inference pipelines
• Implement and experiment with LLMs, GNNs, multi-modal AI, and vision models
• Apply techniques such as quantization, pruning, distillation, sparsity, and parallelism to improve model efficiency
• Work with deployment tools such as ONNX, TensorRT, cuDNN, vLLM, and SGLang for fast and reliable inference
• Design and scale infrastructure on cloud platforms such as AWS, Azure, or GCP
• Collaborate in both academic and entrepreneurial directions – preparing publications, prototypes, and pitch decks
• Participate in strategy development for the formation and early growth of a spin-off company