About Aqemia

Aqemia is a next-generation pharmatech reinventing drug discovery with quantum-inspired physics and generative AI.

Our mission: design innovative small-molecule drug candidates for dozens of critical diseases, faster and smarter, without relying on experimental data.

Unlike traditional approaches, Aqemia starts drug discovery purely in silico. By combining physics-based models with large language models trained on proprietary data, we identify promising molecules with high accuracy before synthesis.

We’ve already delivered multiple preclinical successes and secured strategic partnership. Our internal pipeline is growing fast, with several programs in in vivo optimization.

We’re a team of 65+ based in Paris and London, we bring together chemists, physicists, engineers, and machine learning experts to push the boundaries of what’s possible in early-stage drug discovery.

The Role

As a Scientific Software Engineer in the product team supporting the daily activities of scientists (physics, ML, deep learning) producing predictors and prediction workflows,you’ll contribute to scaling the scientific logic and software that connect Aqemia’s predictive models into advanced drug discovery workflows.

You’ll build the engine that enables large-scale computation and seamless data transformation.

You’ll work at the interface of cheminformatics, software engineering, and platform infrastructure, translating scientific needs into robust, scalable tools used daily across Aqemia’s pipeline.

What you’ll do

What we’re looking for

Preferred mindset

Why Join Us

At Aqemia, 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.