Senior Scientific Software Engineer

Aqemia; Paris; Hybrid; EUR 90,000 - 130,000; Full time

Job summary

AQEMIA is expanding its Engineering team in Paris and London. As a Senior Scientific Software Engineer, you will turn physics-based methods and ML models into scalable software powering our drug discovery platform.

You’ll work with physicists and ML researchers to production‑ise prototypes, shape architecture, and enforce robust engineering standards. This role offers on-site presence in Paris or London with three days on site and two days optional remote, and visa sponsorship options for

Qualifications

• MSc or PhD in computer science, applied mathematics or a related quantitative field, with a background in computational chemistry, molecular simulation or statistical physics.
• 5+ years of software engineering experience, with strong expertise in a cheminformatics context.
• Strong Python skills and experience with RDKit or similar libraries.
• Solid software architecture skills: designing modular, maintainable systems used by several teams.
• Hands‑on experience with CI/CD, containerization (Docker) and workflow orchestration (e.g. Airflow, Prefect, Kubernetes).
• Experience running compute‑intensive workloads at scale on the cloud, including GPUs.
• Ability to work closely with researchers (understanding their methods, challenging prototypes and explaining engineering trade‑offs), with good communication skills and a collaborative mindset.

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.

Skills

Python

RDKit

CI/CD

Docker

Kubernetes

Cloud

Education

MSc or PhD in computer science / math / related quantitative field

Tools

Airflow

Prefect

GitHub Actions

Job description

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 field, with a background in computational chemistry, molecular simulation or statistical physics.
• 5+ years of software engineering experience, with strong expertise in a cheminformatics context.
• Strong Python skills and experience with RDKit or similar libraries.
• Solid software architecture skills: designing modular, maintainable systems used by several teams.
• Hands‑on experience with CI/CD, containerization (Docker) and workflow orchestration (e.g. Airflow, Prefect, Kubernetes).
• Experience running compute‑intensive workloads at scale on the cloud, including GPUs.
• Ability to work closely with researchers (understanding their methods, challenging prototypes and explaining engineering trade‑offs), with good communication skills and a collaborative mindset.

Nice‑to‑Have

• MLOps experience: model versioning, experiment tracking, model serving.
• Experience mentoring engineers or leading technical standards across an organization.

Please note: This role can be based in our offices in Paris or in London, with 3 days on site and 2 days optional remote. We can accommodate visa sponsorship options for candidates in Paris, but not in London.

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.

World‑Class Interdisciplinary Team : work alongside exceptional talent at the intersection of technology and life sciences. Our teams combine deep expertise in AI, physics‑based modeling, biology, and medicinal chemistry to push the boundaries of innovation.

DeepTech Recognition : AQEMIA is proud to be part of the French Tech 40 and France 2030 , highlighting our role as a key player in Europe’s DeepTech ecosystem.

Prime Location with Flexibility : Our offices are located in the heart of Paris and London (King’s Cross) , with flexible work arrangements including up to two remote days per week.

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 and identifying potential inconsistencies or verification signals in application materials based on available information. 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.