AI Research Scientist

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. 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 new data 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 Prior experience working directly on a chemistry, biology, or related physical-science problem, combined with the ability to communicate complex technical concepts clearly to colleagues whose first language is biology or 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) Logistics Compensation is highly competitive. We're also able to sponsor visas for the right candidate. #J-18808-Ljbffr Dayhoff Labs