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
You'll design and train frontier models across biology and chemistry — molecular structure and dynamics, reactions, whole reaction networks. What your models predict decides what the wet labs run next; what the labs find decides your next model. We'll back an approach that might not work if the upside is large enough.
What you'll do
Train large models across three threads: enzyme–substrate prediction, neural network potentials, and inverse design of reaction networks
Own models end to end — architecture, data pipelines, training, debugging, evaluation
Work directly with chemists and biochemists, and translate between the two fields fluently
Fold experimental feedback from partner labs into each iteration
Essential experience
Demonstrated experience training large models end to end, with the depth to discuss in detail what broke and how you fixed it
Strong ML engineering fundamentals: architectures, training dynamics, data pipelines, and evaluation
Ability to communicate complex technical concepts clearly to colleagues whose first language is biology and chemistry rather than AI
Highly preferred
PhD in a quantitative field (common on our team, but depth matters more than credentials)
Experience in ML-for-chemistry or ML-for-biology (e.g., neural network potentials, graph neural networks, protein or reaction models)
Familiarity with computational chemistry or biochemistry concepts and workflows
Logistics
Compensation is highly competitive. We're also able to sponsor visas for the right candidate.
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