The Role
Design and implement infrastructure for ML/deep learning models, ensuring scalability, reproducibility, and performance across the drug discovery lifecycle.
Summary Generated by Built In
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 Senior ML Software Engineer in the DH Team, you will focus on building and scaling the platform-facing product that manages the full lifecycle of Aqemia’s predictive models, our core scientific assets.
You’ll scale and industrialize ML/deep learning models developed by research scientists, so they can be efficiently served to our Drug Hunter community.
You will play a critical role in the development, deployment, and maintenance of single predictors, ensuring they are robust, reproducible, and seamlessly integrated into the platform that drives our drug discovery engine.
This role is deeply technical and foundational to our platform’s performance, reliability, and scalability.
What you’ll do
- Design and implement ML product and infrastructure to support the training, evaluation, deployment, and versioning of predictive models
- Industrialize and optimize ML/deep learning models developed by scientists, making them scalable and cost-efficient in production
- Collaborate closely with scientists to integrate diverse predictors (physics-based, AI-based, hybrid) into the platform
- Ensure reproducibility, traceability, and performance of model pipelines across the full lifecycle
- Develop APIs and tools that expose predictors as scalable services to other teams
- Contribute to software engineering best practices across the ML stack (testing, CI/CD, observability)
- Partner with platform engineers and product stakeholders to ensure technical alignment and delivery
What we’re looking for
- 6+ years of experience in software engineering with strong focus on machine learning systems
- Deep proficiency in Python and ML ecosystem (e.g. PyTorch, scikit-learn, MLFlow)
- Solid understanding of data and model lifecycle management, versioning, and deployment
- Experience building ML infrastructure and model-serving pipelines in production environments
- Familiarity with cloud-based architecture (AWS preferred), containerization (Docker), and orchestration tools
- Ability to work autonomously and lead initiatives with high technical ownership
- Strong communication skills and ability to work closely with scientists and engineers alike
Preferred mindset
- You care about building solid foundations for ML at scale
- You combine scientific curiosity with software engineering rigor
- You enjoy tackling complexity and finding elegant, maintainable solutions
- You thrive in a cross-functional, fast-moving environment where models meet production
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.
Top Skills
Airflow
AWS
Docker
Fastapi
Kubernetes
Mlflow
Python
PyTorch
Scikit-Learn
Snowflake
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The Company
What We Do
AQEMIA is a next-gen pharmatech company generating one of the world's fastest-growing drug discovery pipeline. Our mission is to design fast innovative drug candidates for dozens of critical diseases, such as immuno-oncology.
Our unique approach leverages quantum-inspired physics algorithms to power generative AI in designing novel drug candidates—without relying on experimental data. We already delivered several drug discovery successes within our internal pipeline and through collaborations with pharmaceutical companies.
Our most advanced programs are currently in vivo optimization.
We are growing and hiring! Check our career website: https://jobs.lever.co/aqemia.com
Discover the roles and behind-the-scenes at AQEMIA on our Welcome To The Jungle page: https://www.welcometothejungle.com/fr/companies/aqemia
