Research Scientist, Quantum Chemistry

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. 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 roleYou'll turn quantum-chemistry calculations into kinetic datasets and mechanistic insight our ML models can actually train on. You'll study reaction mechanisms across homogeneous, heterogeneous, and enzymatic systems, and build the protocols that make that data reliable at scale.What you'll doRun DFT and post-HF calculations to study kinetics and mechanism, primarily in homogeneous catalysisBuild and benchmark reproducible protocols for kinetic data generation, with real uncertainty quantificationDesign kinetic datasets for ML training and validation, and set data-quality standards with ML collaboratorsExtend these methods systematically across catalytic systems and reaction conditionsEssential experiencePhD in computational or theoretical chemistry with a catalysis focus, and first-author papers on catalytic mechanismsFluency with a production quantum-chemistry package (Gaussian, ORCA, or similar)Sound DFT judgment for transition-metal systems: functional choice, basis sets, dispersion correctionsHands-on kinetics: transition-state location, IRC, rate constants, free-energy and thermodynamic analysisPython and the computational-chemistry stack (ASE, cclib, RDKit)Highly preferredFirst-author work in homogeneous-catalysis kineticsHeterogeneous (periodic DFT, surfaces, adsorption) or enzyme catalysisAdvanced methods for hard systems: DLPNO-CCSD(T), CASPT2, multireference approachesHigh-throughput workflows, HPC, and automationUncertainty quantification and protocol benchmarkingDataset design and prior collaboration with ML teamsLogisticsCompensation is highly competitive.

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