Research Scientist (Physics)

Physical Superintelligence•Boston, MA

2d•Onsite

About The Position

Physical Superintelligence is a startup with roots at Harvard, MIT, Johns Hopkins, Oxford, the Institute for Advanced Study, and the Perimeter Institute is building AI systems to discover new physics at scale. We are seeking physicists to design evaluation frameworks and verification systems that enable AI-driven physics discovery.

Requirements

PhDs in Physics or related fields.
Deep expertise in at least one major physics domain.
Strong programming skills.
Track record working on challenging, unsolved problems.
Ability to work effectively in fast-paced research environments.
Deep expertise in atomic, molecular, and optical physics, condensed matter physics, plasma physics, fluid dynamics, astrophysics, quantum information, high energy theory, biophysics, soft matter, statistical mechanics or related physics domains.
Understanding of physical validity, conservation laws, and experimental constraints.
Strong programming in Python and C++.
Experience with computational physics simulations and high-performance computing.
Hands-on work with simulation tools such as VASP, Quantum ESPRESSO, LAMMPS, GROMACS, OpenFOAM, COMSOL, or similar platforms.

Nice To Haves

Valued experience at the physics-ML intersection: Differentiable physics, physics-informed machine learning approaches, or surrogate modeling.
Benchmark design, optimization under physical constraints, or verification systems for computational results.

Responsibilities

Convert frontier physics problems into machine-verifiable tasks that AI systems can systematically explore.
Write production code, collaborate with AI researchers and engineers, and ship working systems that enable physics discovery at scale.
Design evaluation frameworks across physics domains including atomic, molecular, and optical physics, condensed matter physics, plasma physics, fluid dynamics, astrophysics, quantum information, high energy theory, biophysics, soft matter, statistical mechanics.
Build verification harnesses that encode physical validity, conservation laws, and experimental rigor to distinguish genuine physics insights from numerical artifacts.
Integrate state-of-the-art physics simulations into AI environments and develop benchmarks testing genuine physics reasoning.
Collaborate with AI researchers on agent architecture and training approaches.
Write production code supporting large-scale discovery workflows.