Research Scientist, Computational Enzymology

Bazeta; Cambridge (MA); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Dayhoff Labs in Cambridge, MA seeks a Research Scientist in Computational Enzymology to simulate enzyme-catalysed reactions from physics up. You’ll map reaction pathways in the active site with QM/MM and free-energy methods, identify transition states, barriers, and residues driving catalysis.

You’ll collaborate with the ML and wet-lab teams, turning results into testable proposals and feeding data back into models while assessing method choices and computational cost.

Qualifications

• Expertise in enzymatic reaction modelling using QM/MM and free energy methods.
• Strong grasp of enzyme catalysis: mechanism, kinetics, cofactors, and active-site chemistry.
• Good understanding of methodological limitations and computational cost.
• Experience in integrating physics-based modelling with machine learning.
• Ability to design informed wet-lab experiments based on simulations.

Responsibilities

• Characterise mechanisms, transition states, and activation barriers in the active site.
• Pin down origins of catalysis and how active-site motion shapes the reaction using MD.
• Turn results into concrete, testable proposals for the wet lab and feed data back into models.
• Collaborate with physics-based and learned methods to strengthen each other.

Skills

QM/MM modelling

MD simulations

Free energy methods

Enzymatic catalysis knowledge

method selection

experimental collaboration

Tools

QM/MM tools

Molecular Dynamics

Free energy calculation tools

Reactive ML potentials

Job description

# Research Scientist, Computational EnzymologyCambridge, Massachusetts2 hours agoID 1614037Price on request## DetailsEmployment type: Full-timeRemote: NoCompany: Dayhoff Labs## DescriptionAbout 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 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 simulate enzyme-catalysed reactions from the physics up. Using reactive and free-energy methods you'll map how reactions proceed in the active site, compute transition states and barriers, and work out where the catalysis actually comes from: why a step has the barrier it does, which residues do the work, and how mutations move it. You'll work with the wider simulation and ML teams, but also directly with our bench scientists — seeing your predictions tested in vitro and getting experimental feedback on fast, tight loops. What you'll do • Characterise mechanisms, transition states, and activation barriers in the active site, and compare computed barriers against measured kinetics • Pin down the origins of catalysis — the residues, interactions, and dynamics that set rate and selectivity — and use MD to understand how active-site motion shapes the reaction • Turn results into concrete, testable proposals for the wet lab, and fold the resulting data back into your models • Help physics-based and learned methods strengthen each other as we build them out Essential experience . • Expertise in enzymatic reaction modelling using QM/MM and a variety of free energy methods • A solid grasp of enzyme catalysis: mechanism, kinetics, cofactors, and how active-site chemistry and conformational dynamics sets rate and selectivity • Sound judgement about method selection, good understanding o methodological limitations and computational cost Highly preferred • Experience in protein design for enzymatic optimization • You've run design–test–refine cycles with a wet lab before • Experience with reactive machine-learned potentials, or coupling physics-based modelling with ML Logistics Compensation is highly competitive. We're also able to sponsor visas for the right candidate.