Applied AI Research Scientist
About this role
About AQEMIA
About our Team
About our Platform Department
The Platform team (~20 people) brings together multidisciplinary teams working on the scientific core of Aqemia’s drug discovery engine. Its mission is to build scalable and reproducible workflows enabling multiple drug discovery programs to run in parallel with minimal manual intervention.
The team combines expertise across Artificial Intelligence and Machine Learning (both research and applications), data science, statistical physics and molecular simulations, computational chemistry (CADD), and other scientific disciplines. Together, they develop predictive models, physics-based simulations, and robust scientific pipelines that power AQEMIA's discovery platform.
At the center of this ecosystem is the “Rocket Launcher” process: an industrialized workflow continuously launching, testing, and improving drug discovery projects through iterative scientific feedback loops.
The role
We are looking for an Applied AI Research Scientist to join Aqemia’s Drug Discovery Platform, working at the intersection of ML and molecular science. You’ll split your time between:
- 50% AI Research: Develop cutting-edge ML models (e.g., GNNs, generative models, physics-informed AI) to predict molecular properties and protein-ligand interactions.
- 50% Applied AI: Build ML models that drive real decisions in our internal and partnered drug discovery programs, working hand in hand with chemists, biologists and project leads.
Responsibilities
- Design and implement novel Deep Learning algorithms (GNNs, generative, physics-based models).
- Take part in cutting-edge research and bibliographic exploration
- Collaborate with research and drug discovery teams to translate models into actionable insights.
- Develop robust ML models (regression, ranking) from molecular and biological data.
- Collaborate with chemists and biologists; deliver results that drive compound prioritization.
- Own ML workstreams from start to finish: goals, timelines and stakeholder communication.
- Deliver models and predictions that scientists can use in their everyday workflows.
AI Research & Development (50%)
Applied ML for Discovery Programs (50%)
Qualifications
- MSc or PhD in Computer Science, Machine Learning, Computational Chemistry, or related field.
- Strong problem-solving skills, autonomy and a collaborative mindset.
- ~3-5 years of experience applying ML in scientific or industrial settings.
- Strong experience with ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).
- Solid Python programming skills and experience working with scientific computing libraries.
Nice‑to‑Have
- Experience in drug discovery, computational chemistry, or physics.
- Experience with generative models (e.g., diffusion models, VAEs, autoregressive models) applied to structured data.
- Familiarity with structure-based drug design and protein-ligand interactions.
- Familiarity with high-performance computing (HPC) and cloud-based ML pipelines.
Why Join Us?
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