Application Scientist

About AQEMIA

AQEMIA is a next-gen pharmatech company generating one of the world's fastest-growing drug discovery pipeline.

Our mission is to design fast innovative drug candidates for dozens of critical diseases, such as immuno-oncology.

Our unique approach leverages quantum-inspired physics algorithms to power generative AI in designing novel drug candidates—without relying on experimental data.

We already delivered several drug discovery successes within our internal pipeline and through collaborations with pharmaceutical companies. Our most advanced programs are currently in vivo optimization.

About the Team

The mission of the Molecular Simulations Team is threefold:

• Support drug discovery programs by maximising the impact of binding free energy calculations in decision-making.
• Build, benchmark and continuously improve reproducible and scalable pipelines for binding free energy calculations, enabling support for multiple programs in parallel.
• Collaborate closely with our Physics Research Team to integrate innovations from Aqemia's proprietary binding free energy methods.

Role Overview

As an Application Scientist in the Molecular Simulations Team, you will work at the interface of molecular modelling, statistical physics, and drug discovery. Your focus will be on applying, implementing and improving Aqemia's physics-based simulation methods to guide medicinal and computational chemistry and help identify high-potential drug candidates.

• Execute molecular simulations (e.g., free energy calculations, Molecular Dynamics) to support drug discovery programs.
• Analyse simulation results to generate insights for project teams.
• Collaborate with computational chemists, biologists, AI researchers and medicinal chemists to design simulation strategies.
• Contribute to the optimisation of internal protocols and workflows.
• Document and present findings to multidisciplinary teams.

• MSc or PhD in Computational Chemistry, Biophysics, Statistical Mechanics, or a related field.
• Hands-on experience with Molecular Dynamics, free energy methods, or protein-ligand modelling.
• Proficiency in Python and simulation tools (e.g., GROMACS, AMBER, OpenMM for instance).
• Strong communication skills and a collaborative mindset.

• Experience in machine learning applications for physics-based modelling.
• Track record of scientific publications and contributions to the computational drug discovery community.

Preferred Mindset

Scientific Rigour: Committed to high standards of accuracy, reproducibility, and method validation.

Humble and Willing to Learn: Open to feedback and continuously seeking to grow scientifically and professionally.

Excited by Challenging Scientific Problems: Motivated by tackling complex, unsolved questions at the frontier of molecular simulation.

Pragmatic and Impact-Driven: Focused on delivering practical outcomes that drive real-world impact in drug discovery.

Why Join Us ?

At AQEMIA, we work for a mission: joining us means having your own impact on changing the way drugs are discovered, and helping to shape the direction of our fast-growing company and team.

Expanding Drug Discovery Pipeline : Focused on critical therapeutic areas like Oncology, CNS, Immuno-inflammation... with in vivo proof of concept/patent stage programs. Collaborations with top Pharma, including a $140M Sanofi deal.

Interdisciplinary Team : brilliant talent from tech and life sciences.

DeepTech Recognition: Part of French Tech 120 and France 2030.

Prime Location : Based in central Paris and London (King's Cross) with the possibility of 2 remote days per week.

International Environment : The work language is English - relocation support and French lessons granted if needed.

Strong Financial Backing : $100M raised from leading European and International investors

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.