Senior Scientific Software Engineer

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 AQEMIA.com , our WTTJ Page, and our LinkedIn.

About our Team

AQEMIA brings together a diverse, multidisciplinary team of 80+ 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.

About our Engineering Department

The Engineering team builds and scales the technical foundations that power Aqemia’s drug discovery engine.

Bringing together expertise across software engineering, cloud infrastructure, data engineering, site reliability engineering (SRE), and scientific computing, the team designs and operates robust, secure, and high‑performance platforms that enable Aqemia’s scientific and AI teams to experiment, train, deploy, and run models at scale.

Their work spans data infrastructure, scientific compute systems, cloud operations, orchestration, observability, CI/CD, developer tooling, and platform scalability. By transforming cutting‑edge research into reliable and scalable systems, the team accelerates the discovery of new medicines while ensuring operational excellence and an outstanding developer experience.

The role

At AQEMIA, our research teams develop cutting‑edge methods, from physics‑based statistical mechanics calculations to fine‑tuned machine learning models for generative chemistry and property prediction.

As a Senior Scientific Software Engineer, you’ll turn these methods into robust, scalable software that powers our drug discovery platform. You’ll sit at the intersection of science and engineering, working closely with physicists and ML researchers to understand their methods, challenge prototypes and bring them into production. You’ll also help shape the architecture and engineering standards of our scientific computing stack, so it stays modular, scalable and maintainable as we grow. This is an opportunity to have a direct impact on how quickly and reliably our science reaches real drug discovery programs.

Responsibilities

• Translate scientific strategies from our StatMech research teams into software components that run at scale.
• Optimize compute‑intensive code (profiling, parallelization, GPU acceleration) and run it reliably at scale on our cloud infrastructure.
• Expose physics‑based calculations through clean APIs that integrate into our technology platform.
• Package ML models fine‑tuned by our AI research teams (generative chemistry, property prediction, etc.) into versioned, reproducible, deployable components, and build scalable, GPU‑efficient inference pipelines.
• Implement MLOps practices, including model and data versioning, experiment tracking, model registry and monitoring.
• Orchestrate ML models and physics‑based calculations into end‑to‑end workflows.
• Co‑lead the architecture of our scientific computing stack and our Python engineering standards: packaging, typing, testing, code review and documentation.
• Contribute to setting up and maintaining CI/CD pipelines and workflow orchestration (e.g. GitHub Actions, Docker, Airflow/Prefect, Kubernetes, Flyte), and stay up to date with advances in cheminformatics.

Qualifications

• MSc or PhD in computer science, applied mathematics or a related quantitative fiel