Staff AI Research Scientist

This means you will be

• Be a technical leader – Drive the development of cutting-edge AI methodologies for molecular modeling and drug discovery.
• Mentor and support the team – Act as a scientific and technical mentor to junior and mid-level researchers, fostering innovation and knowledge-sharing within the team.
• Bridge research and application – Work closely with interdisciplinary teams (AI, computational chemistry, and drug discovery experts) to translate AI research into real-world impact.

Key Responsibilities

• Lead research initiatives to develop and apply novel AI models for molecular modeling, protein-ligand interactions in drug discovery.
• Design and implement advanced machine learning techniques, including deep learning, generative models, graph neural networks, and physics-informed AI to predict molecular properties and interactions.
• Collaborate with interdisciplinary teams, including computational chemists, physicists, and medicinal chemists, to integrate AI-driven solutions into real-world drug discovery pipelines.
• Scale AI models for real-world applications, optimizing model efficiency, interpretability, and generalization to unseen chemical spaces.
• Mentor and guide junior researchers and engineers, fostering a culture of scientific innovation and excellence.

Qualifications

• PhD or equivalent practical experience in AI/ML, Mathematics, Physics, Computational Chemistry or a related field.
• Strong problem-solving skills, autonomy and a collaborative mindset.
• Expertise in machine learning applied to proteins, small molecules, and molecular simulations.
• Strong programming skills in Python and proficiency with ML frameworks (e.g., PyTorch, JAX, TensorFlow).
• Hands-on experience with scientific computing libraries (e.g., RDKit, OpenMM) and molecular representations (e.g., SMILES, molecular graphs, 3D structures).
• Ability to bridge AI research with practical drug discovery challenges, designing scalable and interpretable models.


• Nice-to-Have Qualifications:
• Experience with physics-informed ML models.
• Background in free energy calculations, docking, or molecular property prediction.
• Familiarity with high-performance computing (HPC) and cloud-based ML pipelines.

Preferred Mindset

• Pragmatic and impact-driven – Focused on delivering solutions that work in real-world applications, balancing scientific rigor with practical usability.
• Eagerness to learn – A strong curiosity for scientific advancements and a willingness to continuously expand your expertise.
• Love for highly scientific challenges – Enthusiasm for tackling complex problems at the frontier of AI and drug discovery.
• Synergy minded – A collaborative spirit, thriving in an interdisciplinary environment.
• Humility – Open to feedback and different perspectives, always striving for improvement.