Senior AI Research Scientist

About AQEMIA

AQEMIA is a drug invention company dedicated to creating entirely new medicines to address major unmet medical needs. At the core of our mission is QEMI, our proprietary molecule-invention platform, which uniquely combines cutting-edge science with advanced technology. Powered by physics-based modeling, statistical mechanics, and generative AI, QEMI allows our teams to design novel drug candidates from first principles.

What makes AQEMIA different is our commitment to true innovation: our research is dedicated to the invention of new molecular entities, not the refinement of existing ones. We focus on inventing never-before-seen molecules, without relying on experimental data, and advancing them into a growing pipeline of proprietary programs and strategic partnerships with leading pharmaceutical companies.

Our most advanced preclinical programs are currently in vivo optimization, targeting diseases still waiting for effective treatments, offering our teams the opportunity to work on science that can make a real difference in people’s lives.

For more information, visit  and our Linked In.

AQEMIA brings together a diverse, multidisciplinary team of 65+ professionals based in Paris and London. Our scientists and engineers, including chemists, physicists, machine learning experts, and software engineers, work side by side to push the boundaries of early-stage drug discovery.

This close collaboration across disciplines is central to our approach, enabling us to tackle complex scientific challenges from first principles and translate cutting-edge ideas into novel therapeutic candidates. At AQEMIA, team members are encouraged to contribute their expertise, learn from one another, and play an active role in shaping the future of drug invention.

As a Senior AI Research Scientist, you will join one of Aqemia’s Drug Discovery Platform AI Teams, composed of 5 AI Researchers, and lead efforts to develop cutting‑edge AI algorithms to model protein‑ligand systems and predict molecular properties. You will be a hands‑on technical expert pushing the boundaries of what AI can do for drug discovery. Your deep knowledge of machine learning, strong coding foundations, and applied experience in model development will play a key role in advancing our Drug Discovery Platform.

You will act as a technical mentor, contribute to high‑impact R&D projects, and help integrate innovative AI methodologies into real‑world drug discovery applications.

• Lead the design and the technical implementation of novel ML algorithms with a strong focus on deep learning, generative models, graph neural networks, and physics‑informed AI to predict molecular properties and protein‑ligand interactions.
• Take part in cutting‑edge research and bibliographic exploration. You’ll identify relevant recent publications, benchmark promising ideas, and propose model or pipeline improvements.
• Act as a mentor to junior team members, offering guidance on system design, model implementation, debugging, and good coding practices.
• Collaborate with AI researchers, computational chemists, and drug discovery teams to translate AI models into actionable scientific insights.
• Be deeply involved in coding, from prototyping to deploying production‑ready models. You should have a strong software engineering mindset and a passion for writing clean, efficient, and scalable code.
• Share best practices in deep learning model development, testing, and reproducibility within the team.
• Scale AI models for real‑world applications, optimizing model efficiency, interpretability, and generalization to unseen chemical spaces.

• Strong problem‑solving skills, autonomy and a collaborative mindset.
• PhD or equivalent practical experience in Machine Learning, Computer Science, Applied Mathematics or a related field, with at least 8 years of experience.
• Strong programming skills in Python and proficiency with Deep Learning frameworks (e.g., PyTorch, JAX, Tensor Flow).
• Demonstrated hands‑on experience implementing and deploying deep learning models in production or research environments.
• Strong…