About this role
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 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 do
Run QM/MD simulations combining standard packages with AI-accelerated models
Build reproducible pipelines and benchmarking protocols across QM, MD, and ML
Deploy simulation tools for internal teams; work with software and product on external deployment
Integrate neural network potentials into traditional QM/MD workflows with the ML team
Essential experience
PhD in computational chemistry, chemical physics, materials science, or related field — or a Master's with 3+ years relevant experience
Hands-on experience with QM and MD packages (e.g., VASP, Gaussian, ORCA, GROMACS, LAMMPS, CP2K)
Track record of building computational pipelines and reproducible workflows
Proficiency in Python and scientific computing libraries (NumPy, SciPy, computational chemistry libraries)
Experience with ML frameworks (PyTorch, TensorFlow) and their integration into computational chemistry workflows
Highly preferred
Neural network potentials and modern AI models for molecular simulation (e.g., graph neural networks, transformer models)
HPC environments and workflow management systems
Containerization (Docker) and deployment pipelines
Benchmarking and statistical validation of computational methods
Translating computational insights into practical applications
Logistics
Compensation is highly competitive. We're also able to sponsor visas for the right candidate.