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 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 AQEMIA.com and our LinkedIn.
About our Team
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.
Responsibilities
Design, build, and operate scalable compute systems supporting training, inference, and scientific workloads
Improve the reliability, robustness, and performance of production systems used across research and platform teams
Develop internal tooling to streamline model and workflow lifecycle (build, test, deploy, run).
Drive architectural decisions across compute and data systems in collaboration with engineering and research teams
Improve developer experience for scientists by reducing friction in running and scaling experiments
Establish and promote best practices in system design, observability, and AI-assisted / spec-driven development
Contribute to raising the engineering bar across teams through mentorship and technical leadership
Qualifications
10+ years of experience in software engineering with demonstrated impact at the staff level or equivalent
Strong experience building and operating production systems at scale
Deep expertise in distributed systems or compute-heavy environments
Strong hands-on experience in system architecture and design
Fluent in Python and across the AWS / Postgres / SnowFlake / orchestration stack; comfortable on both sides of the code/infra boundary
Serious hands-on experience with LLM-assisted engineering and spec-driven development - you’ve gone past tooling and built workflow and team practice around it
Strong ownership mindset with the ability to operate across teams and communicate effectively
Nice‑to‑Have
Experience with large-scale compute platforms or infrastructure
Exposure to ML infrastructure or model execution environments
Experience with PyTorch, CUDA, or running custom models in production
Experience designing or operating workflow orchestration systems
Track record of optimizing systems for cost-efficiency at scale
Why Join Us
At AQEMIA, we are driven by a bold mission: transforming the way drugs are discovered. Here, engineers don’t just build software, they help discover real drugs.
You’ll work at the intersection of AI, physics and chemistry, transforming bold scientific ideas into robust, production-grade tools that accelerate discovery.
DeepTech Mission : Build the platform that powers AI-driven drug discovery, combining quantum-inspired physics with generative models
Real-World Impact : Every feature shipped helps scientists prioritize molecules and design better candidates, faster
Modern Stack & Challenges : Python, FastAPI, Airflow, Snowflake, Kubernetes, ML workflows, scientific infra, data engineering at scale
High Ownership, High Impact : Engineers contribute to architecture, tooling, and scientific decision-making
Interdisciplinary Team : Collaborate with chemists, physicists, ML researchers, and product teams
Prime Locations : Central Paris or London offices, with 2 remote days/week
Strategic Traction : Backed by $100M in funding and a $140M partnership with Sanofi
Join us if you’re excited to shape the future of AI-driven drug discovery, and want your code to change the course of real diseases.
