Computational Scientist, Biocatalysis

Descripción del puesto

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 We're looking for someone to build and apply foundational models of enzyme catalysis, with use cases across pharma manufacture, agriculture, and industry. This is pragmatic computational enzyme engineering: you'll build internal models, fine-tune external ones like Boltz, RFdiffusion, and LigandMPNN, and reach for classical biocatalysis methods where they fit. There's no fixed pipeline to inherit — you pick the stack per project and answer for it, and you'll see your designs go into real wet labs on commercial timelines. What you'll do Own the computational side of one to three commercial projects at a time, end to end: substrate analysis, starting-point selection, optimisation strategy, design rounds, in silico characterisation Pick the tool stack per project, defend your choices on technical grounds, and revise them when the data says otherwise Design and train novel architectures Acquire new training data, both computationally and experimentally Work closely with the wet-lab team on assay design, hit-call thresholds, and iteration Essential experience Role-description signals ma