EOI - Computational Materials Scientist
About Atomic Tessellator
Atomic Tessellator is the computational infrastructure for advanced materials, enabling defence and aerospace organisations to rapidly model, test, and optimise materials under extreme constraints.
Our mission is to remove the materials bottleneck so civilisation can advance at the speed of compute.
We’re a seed-stage company with a headcount of five, and have been around for a little over a year. In this time, we have:
- Built a distributed worker architecture to modularise computational materials science operations.
- Scaled machine-learned interatomic potential (MLIP) models to enable multi-GPU inference, letting us model up to 700,000 atoms
- Completed pilot projects across aerospace, defence, nuclear fusion, and advanced polymers.
- Discovered (and are in the process of patenting) two materials, one of which is a high-temperature rare earth magnet substitute.
Everything modern depends on advanced materials, but materials development remains slow, expensive, and heavily constrained by physical trial and error.
Atomic Tessellator is building a CAD-style simulation engine for materials discovery: computational infrastructure that lets organisations design, model, test, and optimise materials before committing to costly real-world experimentation.
We're building a validated predictive engine and deploying it as secure infrastructure for teams that need reliable answers under real operational constraints.
Materials resilience underpins industrial sovereignty and defence readiness. The organisations that can model and deploy advanced materials fastest will shape the next generation of strategic capability.
About the role
Moore's law and MLIPs bring materials science to an inflection point where simulations can demonstrate tangible utility. We're a computational materials science startup that aims to offload as much of the research process to in-silico as possible, and we're pushing to cover the full gamut of engineering materials properties - extending to applications such as neutron irradiation and magnetics, and traversing all scale lengths. Our end-game is inverse materials design.
We're always searching for exceptional people, specifically those that work at the intersection of the following:
- Materials science
- Machine learning
- Computer science
Typically our computational materials scientists roles require strong foundational knowledge in areas such as DFTs and force fields. But what's more important to us is that you can complement our momentum with a strong pace of learning.
If our mission interests you, we encourage you to apply.