Computational Scientist, Biocatalysis

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. Understanding this transition, from geochemistry to biochemistry, is what will let us orchestrate molecular networks and build systems that are more capable, adaptive, and efficient.

If we succeed, the applications are vast: catalysis, green synthesis, ab initio synthetic biology, programmable matter. Understanding and harnessing these processes could let ten billion of us thrive on this planet — and let life keep evolving beyond it.

We're a small, diverse team of AI engineers, computational scientists, and bench scientists. We ship fast and iterate quickly. 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 matter more than CV signals here — a PhD, where you trained, prior industry experience, and Nature papers are all non-essential. We care about:

• A track record of putting computational designs into wet labs and tracking what happened (the worked-to-didn't ratio matters less than whether you can explain the failures mechanistically)
• Fluency across the protein ML stack (at least some of ESM, AlphaFold or Boltz, RFdiffusion, ProteinMPNN / LigandMPNN, docking) and comparable fluency in biocatalysis fundamentals (mechanism, kinetics, common cofactors, expression bottlenecks)
• Good taste in tool selection
• Comfort talking to chemists and fermentation engineers about your model choices in their language

HIGHLY PREFERRED

• Experience driving projects from substrate to characterised design without waiting to be handed the next step
• Scepticism about your own outputs — you'll flag a junk prediction rather than over-claim

LOGISTICS

We're able to sponsor visas for the right candidate.