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.