Role overview
Lila Sciences is the world's first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method. We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at www.lila.ai
If this sounds like an environment you'd love to work in, even if you only have some of the experience listed below, we encourage you to apply.
What you'll work on
- A PhD in either a biological sciences domain (e.g., molecular biology, virology, computational biology or related fields) or a physical sciences domain (materials sciences, physics, chemistry, chemical or nuclear engineering, or related fields), or other related experience.
- Demonstrated ability to set research directions for open problems in dual-use risks in the biological / physical sciences.
- Experience in scientific computing, across either biological or physical sciences.
- Familiarity with dual-use research and dissemination concerns, across the relevant safety / regulatory / governance frameworks (e.g. export control frameworks, biological and chemical-related conventions and controls).
- Ability to communicate complex technical concepts and concerns to non-expert audiences effectively.
- Demonstrated ability to lead teams of internal and external collaborators in buildout of Lila's point-of-view on biological / physical risks
- Demonstrated ability to deal with cross-functional stakeholders (science, AI, product, policy) in a complex environment.
What we're looking for
- Experience in developing or applying ML to biological or physical sciences
- Experience in building evaluations, or conducting red-teaming exercises, across scientific risks for frontier models / narrow scientific tools.
Tags & focus areas
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