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 fundamental physicists, theorists and experimentalists, to run research campaigns on open problems with AI systems, and to make the results verifiable.
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
Run research programs on open problems. Take a problem in your domain, decompose it into questions AI systems can attack, direct the systems, and judge what comes back. You own the problem from formulation to result.
Convert frontier physics problems into machine-verifiable tasks. The hard work is encoding what counts as a correct answer (physical validity, exact bounds, conservation laws) in a form that separates genuine insight from artifact.
Verify AI output. Design the process that separates a genuine result from a plausible-looking artifact, and stand behind results before they leave the building.
Write the manuscripts. Turn results into papers that survive external expert review, and own the path from result to publication.
Collaborate with AI researchers on how agents reason about physics and how to evaluate them. Write production code that ships into discovery workflows running at scale.
What We're Looking For
A PhD in physics, with deep expertise in at least one area of fundamental physics, theoretical or experimental: quantum information, quantum many-body and condensed matter, atomic, molecular and optical, high energy and particle, nuclear, gravity and cosmology, mathematical physics, statistical mechanics. You have produced published or otherwise externally validated results.
Analytic and computational rigor. You derive results and check them, and you know when a numerical hint is not a proof and when a signal is an artifact of the setup. Comfort with symbolic computation and large-scale numerical experiments when the problem calls for them.
Strong Python. 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
Results on named open problems or conjectures in your field.
Formalization, proof assistants, or computer-assisted proof.
Benchmark design or verification systems for physics or mathematics results.
Refereeing for top journals.
Related Roles
If your background is computational or applied physics (CFD, FEA, multiphysics, surrogate modeling, digital twins), apply to Member of Technical Staff, Applied AI instead. That team does applied physics plus AI, and this role is fundamental physics.
How We Work
We hold a high technical bar and give people full ownership of their work, from spec to ship to on-call. We write contracts before logic, test against real systems instead of mocks, and favor simple designs that ship over clever ones that do not. Our development process is AI-native: we work with agentic coding tools daily, write specs that are legible to humans and agents alike, and lead with leverage.
Location and Compensation
This role is based in Boston. We will consider remote candidates on a case-by-case basis. We offer competitive compensation including salary, benefits, and meaningful early-stage equity. We evaluate on physics depth, rigor, intellectual breadth, and shipping velocity. We are an equal opportunity employer and value diverse perspectives in attacking hard problems in science.