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 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 do
Run DFT and post-HF calculations to study kinetics and mechanism, primarily in homogeneous catalysis
Build and benchmark reproducible protocols for kinetic data generation, with real uncertainty quantification
Design kinetic datasets for ML training and validation, and set data-quality standards with ML collaborators
Extend these methods systematically across catalytic systems and reaction conditions
Essential experience
PhD in computational or theoretical chemistry with a catalysis focus, and first-author papers on catalytic mechanisms
Fluency with a production quantum-chemistry package (Gaussian, ORCA, or similar)
Sound DFT judgment for transition-metal systems: functional choice, basis sets, dispersion corrections
Hands-on kinetics: transition-state location, IRC, rate constants, free-energy and thermodynamic analysis
Python and the computational-chemistry stack (ASE, cclib, RDKit)
Highly preferred
First-author work in homogeneous-catalysis kinetics
Heterogeneous (periodic DFT, surfaces, adsorption) or enzyme catalysis
Advanced methods for hard systems: DLPNO-CCSD(T), CASPT2, multireference approaches
High-throughput workflows, HPC, and automation
Uncertainty quantification and protocol benchmarking
Dataset design and prior collaboration with ML teams
Logistics
Compensation is highly competitive. We're also able to sponsor visas for the right candidate.