Computational Scientist, Biocatalysis

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Location: London or Manchester, UK
Start Date: Immediate
Position Type: Full-time

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About the Role

At Dayhoff we reverse engineer the chemistry of life using AI and experiment. In this role you'll build and apply foundational models of enzyme catalysis, with use cases across pharma manufacture, agriculture, and industry. As a member of the AI team, you'll build, maintain, and use frontier models on real-world problems alongside our partners in industry and academia. The work is pragmatic computational enzyme engineering: you'll build and use internal models, work with and fine-tune external ones like Boltz, RFdiffusion, and LigandMPNN, and reach for classical biocatalysis methods where they fit. The goal is to augment both discovery and optimization of enzymes, working closely with our wet lab engineers and foundation model team to deliver for the biocatalysis community.

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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 — there's no fixed pipeline. You 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

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What We're Looking For

Role-description signals matter more than CV signals here. 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

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What doesn't matter:

• Whether you have a PhD. Some excellent people in this field don't

• Where you trained. We've hired well from places we'd never heard of

• Whether you've worked in industry before. The operational stuff we can teach

• Whether you've published in Nature

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Essential Qualities:

• Sceptical of your own outputs: You'll tell us when a prediction is junk, and you won't over-claim.

• Good taste: You pick the right tool for the problem and can say why, not just reach for the newest model.

• End-to-end ownership: You drive a project from substrate to characterised design without waiting to be handed the next step.

• Bilingual: You can hold your own with chemists and fermentation engineers in their language, not just yours.

• Collaborative: You work in tight loops with the wet lab and the foundation model team, and let the data change your mind.

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Why This Role is Different

There's no fixed pipeline to inherit and no house method to defend — you pick the stack per project and answer for it. You'll see your designs go into real wet labs on commercial timelines and find out whether they worked, which is rarer than it should be in this field. Very competitive compensation; the role can be based in London or Manchester, and we sponsor visas where the case is strong.

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Apply

Send a CV and a short note (no cover-letter format required) describing the most recent computational design you put into a wet lab — what happened, and what you'd do differently — to careers@dayhofflabs.com with the subject line "Computational Scientist, Biocatalysis."