About Valence Labs
Valence Labs is Recursion’s frontier AI research engine. We work on high-impact research programs that will materially alter Recursion’s ability to discover and develop medicines for complex diseases. We balance near-term pragmatism with a long-term view on where we believe the field will be in 3-5 years and incubate, design, and productize those approaches that we believe will be most impactful. Our research is driven by optimism, purpose, and a shared vision for a healthier tomorrow, and our work is regularly published in top journals and conferences. Our team is located in London and Montreal, where we share close connections with Mila, the world’s largest deep learning research institute.
About the role
We’re seeking motivated interns to contribute to the development of AI systems across areas, including multi-omic foundation models, next-generation structural biology methods, and approaches for autonomous science. We're looking for individuals with strong engineering skills, including expertise in designing, implementing, improving, and deploying distributed machine learning systems at scale. In addition, we highly value proficiency with state-of-the-art machine learning algorithms and exceptional problem-solving skills.
In this role, you will:
Support Valence Labs’ primary research programs in ML for drug discovery.
Create and improve novel ML methods that will accelerate drug discovery.
Collaborate with an interdisciplinary team of dry and wet lab scientists to inform and improve our models and systems.
Present and communicate research findings through talks, blog posts, publications, and conferences.
A successful candidate will have most of the following:
Currently enrolled in a post-doctoral fellowship, PhD, or Master's degree program.
Strong programming skills and understanding of modern software development practices, especially in Python.
Experience in building and deploying high-performance implementations of deep learning algorithms.
Proven track record in machine learning, including designing new architectures, hands-on experimentation, analysis, visualization, and model deployment.
Demonstrated capability to understand and summarize scientific content and implement deep learning models based on descriptions from publications.
Strong knowledge of linear algebra, calculus, and statistics.
Passion for applying ML research to real-world problems.
Nice to have:
Authorship of a publication in peer-reviewed conferences (e.g., NeurIPS, ICML, ICLR, or similar).
Contribution to high-visibility ML codebases.
Scientific knowledge of biology, chemistry, or physics along with previous experience working in a scientific environment across disciplines.
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.
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