Machine Learning Research Scientist in Slough

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’re seeking an experienced ML Research Scientist to drive bold, ambitious research agendas across Valence Labs’ primary research programs, including multi-omic foundation models, next-generation structural biology and atomistic modeling, and approaches for autonomous science. We’re looking for individuals who can articulate and execute a research vision, lead long-running technical projects, and work fluidly across disciplines. You’ll combine mastery of modern machine learning with strong scientific intuition and exceptional engineering skills to develop AI systems that meaningfully accelerate drug discovery.

In this role, you will:

• Lead and contribute to frontier research programs in ML for drug discovery, including generative models, multi-omic representation learning, and atomistic/structural modeling.
• Own a research agenda end-to-end: ideation, implementation, experimentation, evaluation, and deployment in collaboration with Recursion’s platform teams.
• Collaborate closely with interdisciplinary teams of ML researchers, software engineers, wet-lab scientists, and domain experts to identify high-value research opportunities.
• Communicate findings internally and externally through talks, publications, blog posts, and conference presentations.
• Contribute to the broader scientific community through open-source, open-science, and collaboration initiatives.

A successful candidate will have most of the following:

• 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.
• A proven track record of impactful machine learning research, including designing new neural networks to model molecular systems, proposing new theories, improving upon existing ideas, and applying novel ML techniques to real-world problems.
• Strong technical and engineering skills, including ability to rapidly prototype ML models.
• Comfort working cross-functionally with interdisciplinary teams of dry and wet scientists.
• Experience in project supervision, leadership, or management, including lead authorship in publications at peer-reviewed conferences (e.g., NeurIPS, ICML, or ICLR) and/or journals (e.g. Nature, Science, JACS, or ACS).

Valence Labs is committed to creating a diverse and inclusive environment, where understanding and accommodating personal needs and preferences is a priority. Join our multidisciplinary team of passionate researchers, eager to push the boundaries of ML research and contribute to industrializing scientific discovery to radically improve lives.

This is an office-based, hybrid position at one of our offices located in Montreal, Quebec, or London, England. Employees are expected to work in the office at least 50% of the time.

At Recursion, we believe that every employee should be compensated fairly. Based on the skill and level of experience required for this role, the estimated current annual base range for this role is £118,000 to £137,000 (GBP). You will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package.