Aqemia

Aqemia is a France-based AI platform that offers drug discovery solutions for the pharmaceutical sector.

aqemia.com

Aqemia

Aqemia is a France-based AI platform that offers drug discovery solutions for the pharmaceutical sector.

aqemia.com

HQFR

Team Size71

Open Jobs1

Total Funding$133M

Latest FundraiseUnknown

TL;DR

Founded: 2019

Headquarters: Paris, France

Tech: Quantum-inspired physics algorithms + generative AI (QEMI platform)

Therapeutic focus: Oncology and immuno-oncology

Employee count (reported): 71

Total disclosed funding: >$100M (reported ~$132.75M)

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Company Overview

Problem Domain

Drug discovery for oncology and immuno-oncology

Founded

2019

Industry

Research Services

Tech Stack

Quantum-inspired/physics algorithms

Generative AI

Physics-driven molecular modeling

Funding Track Record

Series A (extension)- 2024-01-30

€30,000,000

Reported Series A extension; sources report Series A reached ~€60M by January 2024 when combined with earlier amounts.

Series A (or follow-on)- 2024-12-10

$38,000,000

Reported round announced December 10, 2024 bringing cumulative disclosed funding past $100M.

Grant / public program- 2025-04-01

€7,400,000

Reported public grant/program investment listed under France 2030.

Investor Signal

“Includes institutional and public backers such as Bpifrance, Eurazeo, Elaia, Wendel Growth and France 2030”

Founders

What we do

Join the Team

Principal Data Scientist

On-SiteParis, FR

On-Site • Paris, FR

Who you are

PhD (or equivalent experience) in Data Science, Physics, Applied Mathematics, Computational Chemistry, or related fields
10+ years of experience applying advanced data science methods to complex scientific problems
Proven leadership in driving multi-stakeholder, high-impact scientific projects
Strong programming skills in Python and experience with scientific computing
Deep expertise in data analysis, statistical modeling, bayesian optimisation and decision-making in R&D environments
Strategic mindset with the ability to influence technical direction across teams
Experience in virtual screening, cheminformatics, or molecular modeling
Familiarity with tools like RDKit, SMILES, OpenMM, or experience working with molecular simulation outputs
Knowledge of high-performance computing or cloud-based scientific workflows
Pragmatic and Impact-Driven - Focused on delivering solutions that work in real-world applications, balancing scientific rigor with practical usability
Eagerness to Learn - A strong curiosity for scientific advancements and a willingness to continuously expand your expertise
Love for High Scientific Challenges - Enthusiasm for tackling complex problems at the frontier of AI and drug discovery
Team-Oriented - A collaborative spirit, thriving in an interdisciplinary environment
Humility - Open to feedback and different perspectives, always striving for improvement

What the job involves

You will join the Platform department, AQEMIA’s department responsible for developing and running methods to accelerate the search for molecules.

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As Principal Data Scientist, your mission is to lead and scale data science initiatives that guide decision-making in virtual screening, hit optimisation and hit-to-lead development.

You will contribute across multiple projects within the Platform organisation and collaborate with scientific and technical teams to define and improve AQEMIA’s data-driven approaches

Design and improve data-driven methods to benchmark and optimise in silico screening techniques

Develop models (bayesian optimisation for instance) and analyses to configure optimal screening workflows for each target

Guide compound selection strategies through quantitative, evidence-based approaches

Mentor and support data scientists within the Platform team, promoting scientific excellence and skill development

Contribute to cross-functional technical strategy, working closely with engineers and ML scientists to improve tools, workflows, and model reliability across AQEMIA’s platforms

Drive applied research projects in areas such as uncertainty quantification and propagation within Binding Free Energy pipelines

Promote cross-department collaboration, aligning Platform’s data efforts with evolving needs in Chemistry, ML, and Engineering