Research Engineer, Accelerated 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 build simulation pipelines that fuse conventional computational chemistry with AI-accelerated models, in a setting where the simulation and the experiment are on the same clock. Build it, deploy it, watch it get tested - often in the same month.What you'll doRun QM/MD simulations combining standard packages with AI-accelerated modelsBuild reproducible pipelines and benchmarking protocols across QM, MD, and MLDeploy simulation tools for internal teams; work with software and product on external deploymentIntegrate neural network potentials into traditional QM/MD workflows with the ML teamEssential experiencePhD in computational chemistry, chemical physics, materials science, or related field - or a Master's with 3+ years relevant experienceHands-on experience with QM and MD packages (e.g., VASP, Gaussian, ORCA, GROMACS, LAMMPS, CP2K)Track record of building computational pipelines and reproducible workflowsProficiency in Python and scientific computing libraries (NumPy, SciPy, computational chemistry libraries)Experience with ML frameworks (PyTorch, TensorFlow) and their integration into computational chemistry workflowsHighly preferredNeural network potentials and modern AI models for molecular simulation (e.g., graph neural networks, transformer models)HPC environments and workflow management systemsContainerization (Docker) and deployment pipelinesBenchmarking and statistical validation of computational methodsTranslating computational insights into practical applicationsLogisticsCompensation is highly competitive.

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