Research Scientist, Computational Enzymology

United States Digital Space LLC; Cambridge (MA); On-site; USD 130,000 - 200,000; Full time

Benefits offered by this job

Visa sponsorship

Competitive compensation

Job summary

United States Digital Space LLC in Cambridge, MA is seeking a computational biochemist to simulate enzyme-catalysed reactions from first principles, map transition states, and quantify activation barriers using QM/MM and free-energy methods.

You will collaborate with experimentalists and ML teams, turning results into actionable wet-lab proposals, and improve physics-based and learned models. Visa sponsorship available; compensation highly competitive.

Qualifications

• Expertise in enzymatic reaction modelling using QM/MM and free-energy methods.
• Strong understanding of enzyme catalysis, kinetics, cofactors, and active-site chemistry.
• Ability to select appropriate methods balancing cost and accuracy.

Responsibilities

• Characterise mechanisms, transition states, and activation barriers in the active site.
• Use MD to understand how active-site motion shapes the reaction and catalysis origins.
• Turn results into testable proposals for the wet lab and fold data back into models.
• Help physics-based and learned methods strengthen each other as we build them out.

Skills

QM/MM modelling

Free energy methods

Enzyme catalysis knowledge

MD simulations

Education

PhD in Chemistry/Bioinformatics or related field

Job description

About usWe'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 roleYou'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.