AI Scientist – AI-Driven Target Identification

Key responsibilities:
- Design, develop, and apply advanced AI/ML approaches to extract actionable insights from pre-clinical, clinical and real-world evidence datasets.
- Demonstrate value of innovative AI techniques in drug target identification, biomolecular interaction modeling, drug development and biomarker discovery.
- Work with foundational models (pre-trained, self-supervised, multi-purpose, and multi-modal) to advance generative AI applications in drug discovery.
- Collaborate with cross-functional teams to develop and adopt best practices for ML-ready data.
- Contribute to scientific publications and present results at internal and external scientific conferences.

Requirements:
- Ph.D. in Machine Learning, Computer Science, Applied Mathematics, Computational Biology or related field.
- Strong experience in one or more: generative AI, biomedical foundation models, geometric deep learning, multi-modal learning, large-scale knowledge graphs.
- Excellent programming skills; deep learning frameworks such as PyTorch; openness to learning new tools/technologies.
- Practical experience across ML and LLM software stack (feature engineering, model development, deployment, validation).
- Prior experience with omics data and familiarity with oncology drug development.
- Excellent communication skills for conveying complex insights to cross-functional teams.
- Demonstrated research skills via publications in top-tier ML/AI conferences and/or leading scientific journals.