Senior AI Scientist - Molecular Discovery

Senior AI Scientist - Molecular Discovery

Pierre Fabre

Toulouse, France

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.

Who you are?

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.