Senior AI Scientist- Molecular Discovery- CDI- F/M

INSTITUT DE RECHERCHE PIERRE FABRE SAS; Pechbusque; Sur place; EUR 60 000 - 80 000; Plein temps

Avantages offerts par ce poste

Incentives and profit-sharing

Health and provident insurance

16 days of holidays (RTT)

Public transport participation

Résumé du poste

INSTITUT DE RECHERCHE PIERRE FABRE SAS is seeking a Senior AI Scientist – Molecular Discovery in Pechbusque. The successful candidate will design and maintain AI workflows to enhance drug discovery processes. You will lead the development of machine learning methodologies for molecular optimization, focusing on oncology.

Qualifications include a PhD in a relevant field and expertise in machine learning and advanced programming. The position offers competitive benefits, including incentives and health insurance, with flexibility for remote work.

Qualifications

• PhD in a relevant Computational Science.
• Proficient in designing deep learning architectures.
• Knowledge of AI-driven structural prediction.
• Expert in Python and its scientific ecosystem.

Responsabilités

• Design and implement AI-driven discovery architectures.
• Develop workflows for accurate structure prediction.
• Automate computational tasks and ensure reproducibility.
• Act as a bridge between medicinal chemistry and data science.

Connaissances

Machine Learning Expertise

Advanced Programming

Physics-Aware AI

Chemistry & Drug Design Awareness

Software Engineering

Formation

PhD in Computer Science, Artificial Intelligence, Cheminformatics

Outils

Python

PyTorch

NumPy

SciPy

Description du poste

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 for chemical space exploration and molecular optimization within our oncology pipeline.

Key Responsibilities

• 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, 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, using deep learning methods to accelerate or replace traditional molecular dynamics, enabling 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.

Qualifications

• 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 developing 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.

Benefits

• Incentives and 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...

EEO Statement

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.

Location

The position is based in Toulouse, with plenty of flexibility for remote work.