Computational Scientist, Origin & Evolution of Life

Job description

About us We're reverse-engineering the origin of life — one of the great unsolved problems in science, and one we think AI finally makes tractable. We believe that understanding this transition, from geochemistry to biochemistry, will let us orchestrate molecular networks and build systems that are more capable, adaptive, efficient, and intelligent. If we succeed, the applications are vast: from catalysis and green synthesis to ab initio synthetic biology and programmable matter. Understanding and harnessing these processes could let ten billion of us thrive on this planet — and let us dream that diverse life keeps evolving and thriving beyond it. We're a small, diverse team of AI engineers, computational scientists, and bench scientists. We hold ourselves to the rigor of a research institute, but we ship like an engineering firm. Global team, HQs in Cambridge, MA and London, UK. The role You'll run your own research line inside our central bet: that the origin of life is the missing chapter of biochemistry — the unfilled gap between geochemistry and modern enzymology. Questions we're interested in include: How did catalysts emerge from prebiotic chemistry, and what does that say about the enzyme fitness landscape now? What did the reaction networks before biochemistry look like? How do thermodynamic constraints shape complex chemical systems, and what drove enzymes toward specificity? What you'll do Drive an independent research programme to insight and publication-quality results within our origins-of-life mission Pursue questions spanning prebiotic catalysis, chemical reaction networks, thermodynamic constraints, and the evolution of enzymatic specificity Collaborate closely with AI researchers, computational chemists, and wet-lab scientists Essential experience PhD and publication record in computational biology, computational chemistry, systems biology, chemical engineering, or an adjacent field Real depth in at least one of: chemical reaction network modelling; ML for molecular systems; computational biochemistry (QM/MD, reaction or catalysis modelling); evolutionary modelling (phylogenetics, ancestral reconstruction, population genetics); non-equilibrium thermodynamics or statistical physics Ability to drive a research programme to insight and publication-quality research (this matters more than specific research experience) Highly preferred Cross-di…