Overview
Physical Superintelligence is a startup with roots at Google, NVIDIA, Harvard, Meta, MIT, Oxford, Johns Hopkins, Cambridge, and the Perimeter Institute building AI systems to discover new physics at scale. We are seeking engineers to build platform infrastructure at the intersection of computational science, AI systems, and software engineering.
Our mission is to discover and commercialize transformative physics breakthroughs at scale with artificial superintelligence, safely, verifiably, and for broad public benefit.
The last century's golden age of physics gave us transistors, lasers, and nuclear energy. We believe artificial superintelligence will unlock the next one. We're creating the infrastructure to industrialize scientific discovery and usher in this new era.
We have one product: new physics, at scale.
Role and
Responsibilities
- Convert frontier physics problems into machine-verifiable tasks that AI systems can systematically explore. The hard work is encoding physical validity, conservation laws, and experimental rigor in a way that distinguishes genuine insight from numerical artifact.
- Design evaluation frameworks and verification harnesses across your physics domain. Build benchmarks that test real physics reasoning, not pattern matching, and that scale across the breadth of problems PSI cares about.
- Integrate state-of-the-art physics simulations into AI environments. Make computational physics legible to agents and useful to researchers.
- Collaborate with AI researchers on agent architecture, training, and evaluation. Write production code that ships into discovery workflows running at scale.
What We're Looking For
- A PhD in physics or a closely related quantitative discipline, with deep expertise in at least one physics domain (e.g., atomic and molecular, condensed matter, plasma, fluid dynamics, astrophysics, quantum information, high energy, biophysics, soft matter, statistical mechanics). You have produced verifiable, published, or otherwise externally validated results.
- Strong programming skills in Python and hands-on experience running and extending physics simulations (CFD, molecular dynamics, FEA, quantum, or comparable), with comfort in high-performance computing environments. You can read and write production code, not just notebooks.
- A track record working on hard, unsolved problems in fast-paced research environments. You ship; you do not stall.
- Fluency at the physics-ML intersection: you do not need to be an ML expert, but you can read a modern ML paper, reason about what an agent is doing, and have an opinion on how to evaluate it.
Nice to Have
- Hands-on experience with specific simulation platforms such as VASP, Quantum ESPRESSO, LAMMPS, GROMACS, OpenFOAM, COMSOL, or comparable.