Research Scientist, Next-Generation Structural Biology & Atomistic Modeling

About Valence Labs Valence Labs is Recursion’s frontier AI research engine. We lead high-impact research programs designed to materially expand Recursion’s ability to discover and develop medicines for complex diseases. Our team balances near-term pragmatism with a long-term view of where the field is heading in the next 3–5 years, incubating, designing, and productizing the approaches we believe will define the future of drug discovery. Our work is driven by optimism, purpose, and a shared vision for a healthier tomorrow. We publish in top journals and conferences, contribute to open science, and engage with some of the world’s most active ML-for-drug-discovery research communities. Our teams are based in London and Montreal, with deep ties to Mila, the world’s largest deep-learning research institute. About The Role We are seeking a Research Scientist with a hybrid research-engineering mindset to join our team. In this role, you will be at the forefront of developing generative architectures and foundation models that ground machine learning in real-world physical and biological discovery. You will focus on accelerating and improving the accuracy of molecular design and structural biology workflows—specifically targeting the intersection of physics-informed frameworks and data-driven ML to solve complex protein-ligand interaction challenges. Key Responsibilities Model Innovation : Research and develop state-of-the-art architectures (e.g., flow matching, diffusion models, geometric deep learning) tailored to modeling protein-ligand interactions. Physics-ML Integration : Develop hybrid approaches that integrate co-folding, molecular dynamics (MD), and experimental potency data to achieve high-resolution accuracy on novel targets. Scalable Engineering : Build and maintain ML systems capable of processing massive datasets, such as protein-ligand simulations, on high-performance compute clusters (BioHive). Biological Grounding : Ensure ML predictions are biologically t