Research Scientist, Next-Generation Structural Biology & Atomistic Modeling

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.

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.

• →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 trustworthy and actionable by collaborating closely with drug discovery teams to reduce cycle periods and dead ends in lead optimization.
• →Open Science & Collaboration: Publish findings in top-tier venues (e.g., NeurIPS, ICML, Nature, JACS) and contribute to the broader scientific community.

• PhD (or equivalent) with significant academic or industry research experience in machine learning applied to structural biology, atomistic modeling, or physical simulation.
• Scientific knowledge of physics and chemistry, with a deep understanding of physical constraints and invariances in molecular systems.
• Impactful research track record, including experience with equivariant models, generative modeling of molecular systems, or replacing traditional physics workflows (like ABFE) with ML-driven alternatives.
• Strong technical and engineering skills, including proficiency in Python and the ability to build scalable, reproducible experiment pipelines.
• Interdisciplinary empathy, with a proven ability to work effectively with medicinal chemists and biophysicists to ensure models solve real-world drug discovery problems.
• Leadership and communication skills, including the ability to explain complex ideas clearly to both technical and non-technical stakeholders.