Senior AI Scientist, Molecular Discovery- F/M

Pierre Fabre Laboratories; Toulouse; Hybrid; EUR 60,000 - 80,000; Full time

Benefits offered by this job

Incentives and profit-sharing

Health and provident insurance

25 days of personal holidays

Public transport participation

Job summary

A leading pharmaceutical company based in Toulouse is seeking a Senior AI Scientist in Molecular Discovery. This role involves designing AI workflows and data-driven protocols to enhance drug discovery efforts. The ideal candidate should have a PhD in a relevant field and extensive experience in machine learning and programming, with a focus on drug design. The role offers a competitive remuneration package and the flexibility for remote work, making it an attractive opportunity for experts in the field.

Qualifications

• PhD in a relevant field with a track record in molecular systems or drug design.
• Proficiency in deploying deep learning architectures for structural biology.
• Experience integrating physics with ML models.

Responsibilities

• Design and implement automated workflows using AI-driven methods.
• Develop protocols for molecular physics and protein-ligand conformations.
• Work across teams as a technical support bridge between disciplines.

Skills

Machine Learning Expertise

Physics-Aware AI

Advanced Programming

Chemistry & Drug Design Awareness

Software Engineering

Communication skills

Education

PhD in Computer Science, Artificial Intelligence, Cheminformatics, or related

Tools

Python

PyTorch

NumPy

SciPy

RDKit

Git

Job description

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 computational 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.

This position is based in Toulouse (Oncopole, Langlade site) with plenty of flexibility for remote work.

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...

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.