AI Research Scientist

AI Research Scientist – Biological Foundation Models Paris (hybrid) | €80,000–€100,000 + equity

We’re partnering with an innovative deeptech company at the forefront of AI-driven drug discovery in immunology . Their mission is to tackle one of the biggest challenges in pharma R&D — bridging the gap between preclinical research and clinical success using cutting-edge foundation models.

This is a unique opportunity to work on large-scale, multimodal biological data and contribute to a next-generation AI platform designed to predict patient outcomes and accelerate therapeutic development.

The Role

As an AI Research Scientist , you will design, train, and scale deep learning models applied to complex biological systems. You’ll work on multimodal transformer architectures across transcriptomics, histology, and clinical data — pushing the boundaries of biological foundation models.

You’ll collaborate closely with a multidisciplinary team of ML researchers, computational biologists, and immunologists, translating model outputs into real-world impact across drug discovery and development.

What You’ll Be Doing

• Design and train transformer-based foundation models on large-scale biological datasets

• Develop pre-training and fine-tuning strategies across species, tissues, and diseases

• Work on multimodal learning (omics, imaging, clinical data)

• Run scaling experiments and optimise model performance

• Contribute to and lead publications in top-tier ML and computational biology venues

• Apply models to real-world challenges: target validation, biomarker discovery, and patient stratification

What We’re Looking For

• PhD in Machine Learning, Computer Science, Applied Mathematics, or similar

• 3+ years’ experience in deep learning research

• Strong publication record (e.g. NeurIPS, ICML, ICLR, AAAI)

• Expert Python skills with PyTorch experience

• Experience training models on large-scale datasets

Nice to Have

• Experience with biological or multi-omics data

• Background in foundation models, self-supervised or multimodal learning

• Experience training large-scale models (>300M parameters)

• Comfortable working in fast-paced, high-growth environments

If you’re excited about applying cutting-edge AI to solve real-world biological problems, this is a rare opportunity to make a tangible impact in drug discovery.

Feel free to reach out for more details or a confidential discussion.