Research Scientist (Physics)

Research Scientist (Physics) Research Scientist (Physics) Overview 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. Role and Responsibilities Convert frontier physics problems into machine-verifiable tasks that AI systems can systematically explore. This role requires writing production code, collaborating with AI researchers and engineers, and shipping 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 What We're Looking For We seek candidates with PhDs in Physics or related fields who have deep expertise in at least one major physics domain and strong programming skills. You should have a track record working on challenging, unsolved problems and the ability to work effectively in fast-paced research environments. Physics domain expertise: 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 Programming and computational skills: Strong programming in Python and C++ with 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 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 Location and Compensation This is an in-person role based in Boston or San Francisco. We offer competitive compensation including salary, benefits, and meaningful early-stage equity. We evaluate on physics depth, intellectual breadth, and shipping velocity. We are an equal opportunity employer and value diverse perspectives in attacking hard problems in science.