Generative AI and Multi-Agent Systems Research Engineer (M/F)
Confirmed on the employer's own hiring board on Oct 18, 2026. First seen by Alion on Aug 27, 2026.
Contexte
Institut Curie is recruiting a Research Engineer or Scientist in Artificial Intelligence to join the AGORA project (AGentic Orchestration for Research and Clinical Applications in Oncology).
The ambition of AGORA is to design and validate one of the first agentic AI platforms fully dedicated to biomedical research and precision oncology. The platform will be capable of exploring complex biological data, integrating the latest medical knowledge, assisting molecular interpretation, and generating novel research hypotheses. To achieve this goal, AGORA combines large language models (LLMs), biomedical knowledge bases, bioinformatics tools, multi-omics datasets, and clinical data into a unified reasoning framework.
The project brings together experts in artificial intelligence, machine learning, and data science, specialists in health data integration and governance, researchers in systems biology and mechanistic modelling, as well as oncologists actively involved in precision medicine programmes.
The successful candidate will work at the interface of these different groups and will join the Bioinformatics Core Facility and INSERM U1331 Computational Oncology Unit, a highly interdisciplinary environment comprising approximately 100 researchers working in computational biology, bioinformatics, and artificial intelligence.
Main Responsabilities
The position is central to the project and aims to develop the scientific and technological foundations of a new generation of AI assistants for oncology.
As a Generative AI and Multi-Agent Systems Engineer (M/F), you will:
· Design and develop multi-agent architectures for biomedical applications.
· Evaluate and benchmark large language models and agentic AI frameworks.
· Develop workflows integrating:
- multi-step reasoning,
- Retrieval-Augmented Generation (RAG),
- bioinformatics and biological modelling tools,
- machine learning and predictive AI models.
· Connect AI agents with biomedical knowledge bases, analysis pipelines, and HPC resources.
· Define evaluation protocols assessing response quality, explainability, robustness, and reliability.
· Collaborate closely with biologists, clinicians, bioinformaticians, and data engineers.
· Contribute to scientific publications and future research projects in AI and precision oncology.