Pierre Fabre is the 2nd largest dermo-cosmetics laboratory in the world the 2nd largest private French pharmaceutical group and the market leader in France for products sold over the counter in pharmacies.

Its portfolio includes several medical franchises and international brands including Pierre Fabre Oncologie Pierre Fabre Dermatologie Eau Thermale Avène Klorane Ducray René Furterer A-Derma Naturactive Pierre Fabre Oral Care.

Established in the Occitanie region since its creation and manufacturing over 95% of its products in France the Group employs some 10000 people worldwide. Its products are distributed in about 130 countries.86% of the Pierre Fabre Group is held by the Pierre Fabre Foundation a government-recognized public-interest foundation while a smaller share is owned by its employees via an employee stock ownership plan.

In 2019 Ecocert Environment assessed the Groups corporate social and environmental responsibility approach in accordance with the ISO 26000 sustainable development standard and awarded it the Excellence level.

Pierre Fabre is recognized as one of the Worlds Best Employers 2021 by Forbes. Our group is ranked in the Top 3 in the cosmetics industry and in the Top 10 in the pharmaceutical industry worldwide.

Your mission

We are seeking a Senior AI Scientist- Molecular Discovery to design build and maintain generative AI workflows and data-driven protocols to support our drug discovery programs.

The successful candidate plays a pivotal role in accelerating the identification of high-quality drug candidates by leading the development of machine learning methodologies to chemical space exploration and molecular optimization within our oncology pipeline.

Your role within a pioneering company in full expansion:

AI-Driven Discovery Architecture: Designing and implementing robust automated pipelines that combine AI-driven methods with physics-based simulations.

AI-Physics Integration & Structural Modeling: Bridge the gap between machine learning and molecular physics by developing workflows for the accurate structure prediction of biomolecular interactions. This involves implementing state-of-the-art diffusion models to characterize the interactions between proteins nucleic acids and small molecules.

AI-Driven Conformational Sampling: Implement and refine AI-based protocols for sampling protein-ligand conformations. This includes utilizing deep learning methods to accelerate or replace traditional molecular dynamics enabling the rapid exploration of the conformational landscape with high fidelity.

Methodological Innovation: Evaluating and incorporating emerging methods from peer-reviewed literature to enhance the accuracy and efficiency of the discovery platform.

Protocol Automation: Developing production-grade Python scripts and libraries to automate routine computational tasks ensuring reproducibility across all discovery programs.

Cross-Functional Technical Support: Acting as a technical bridge between medicinal chemistry and data science ensuring that automated protocols are effectively applied by the computational chemistry team.

This position is based in Toulouse (Oncopole Langlade site) compatible with teleworking up to 2 days a week according to company rules.

We offer an attractive remuneration/benefits package: Incentives profit-sharing Pierre Fabre shareholding with matching contribution health and provident insurance 16 days of holidays (RTT) in addition to 25 days of personal holidays public transport participation very attractive CE...

Your skills at the service of innovative projects:

Education: A PhD in Computer Science Artificial Intelligence Cheminformatics or a related Computational Science. Candidates from a CS/DS background should demonstrate a significant track record of applying these techniques to molecular systems or drug design.

Machine Learning Expertise: Proven proficiency in designing or deploying deep learning architectures specifically diffusion models Graph Neural Networks (GNNs) or Attention-based models applied to structural biology or chemistry.

Physics-Aware AI: Experience with the integration of physical principles into ML models including knowledge of AI-driven structural prediction and techniques for sampling biomolecular ensembles.

Advanced Programming: Expert-level command of Python and its scientific/AI ecosystem (PyTorch PyTorch Geometric RDKit NumPy SciPy) for the development of complex discovery workflows.

Chemistry & Drug Design Awareness: A strong understanding of chemical informatics and the ability to integrate physical constraints such as synthetic accessibility or valency into machine learning frameworks.

Software Engineering: Strong experience in Unix/Linux environments high-performance computing (HPC) management and professional version control practices (Git).

Communication: Excellent written and oral communication skills with the ability to document technical protocols clearly for a multi-disciplinary audience.

We are convinced that diversity is a source of fulfillment social balance and complementarity for our employees which is why our offers are open to all without restriction.