Machine Learning Research Scientist

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 biological discovery. 

• PhD (or equivalent) with significant academic or industry research experience in a related technical field involving machine learning applied to drug discovery.
• Scientific knowledge of biology, chemistry, or physics, along with previous experience working in a scientific environment across disciplines.
• Impactful research track record, including designing new neural networks to model molecular or biological systems, proposing new theories, or applying novel ML techniques to real-world problems.
• Strong technical and engineering skills, including the ability to rapidly prototype ML models (Python proficiency required; Rust preferred for high performance molecular encoding or data pipelines).
• Leadership and communication skills, including a lead authorship record in peer-reviewed conferences (e.g., NeurIPS, ICML, ICLR) or journals (e.g., Nature, Science, JACS).
• Interdisciplinary empathy, with a proven ability to work effectively with interdisciplinary teams of dry and wet scientists.

• →Model Innovation: Research and develop state-of-the-art architectures (e.g., flow matching, diffusion models, geometric deep learning) tailored to specific biological or chemical challenges.
• →Scalable Engineering: Build and maintain ML systems capable of processing massive datasets on high-performance compute clusters (BioHive).
• →Biological Grounding: Ensure ML predictions are biologically trustworthy and actionable by collaborating closely with drug discovery teams.
• →Open Science & Collaboration: Publish findings in top-tier venues and contribute to the broader scientific community.