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# Senior Scientific Software Engineer

- **Company:** AQEMIA
- **Location:** Paris, France
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Cloud Computing, Code Review, Continuous Integration, Information Engineering, Data Infrastructure, Programming Tools, Github, Python (Programming Language), Machine Learning, Molecular Modeling, Software Architecture, Reliability Engineering, Scientific Computating, Software Engineering, Management of Software Versions, Graphics Processing Unit (GPU), Parallel Computation, Kubernetes, Information Technology, Machine Learning Operations, Model Registry, Software Version Control, Docker
- **Published:** October 31, 2026
- **Apply:** https://www.welcometothejungle.com/en/companies/aqemia/jobs/senior-scientific-software-engineer_paris_b54yhu4w

## About the Role

* 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.

## Description

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., * 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.