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
- Build and train AI agents and training systems that learn to do physics. Focus on the core research questions: how agents acquire physical reasoning, how to design action spaces for scientific tool use, how to structure rewards that survive long-horizon discovery tasks, and how training infrastructure scales without breaking the science.
- Design evaluation workflows and benchmarks for physics reasoning. Distinguish genuine reasoning from pattern matching and benchmark gaming. Build the instrumentation that makes agent behavior interpretable, not opaque.
- Publish results that advance the field of AI for science. Develop training curricula, reward structures, and architectures for discovery tasks; iterate on what works in practice; share what works at top ML venues where it serves the mission.
- Collaborate with physicists who design verification harnesses and with engineers who build training infrastructure. Ship working systems end-to-end, not isolated research artifacts.
- HWhat We're Looking For
- PhD in machine learning, computer science, physics, mathematics, or a related quantitative field, with a track record of recent publications at top venues (NeurIPS, ICML, ICLR, or comparable physics-ML venues). You have produced original research that the community recognizes.
- Hands-on track record building agents and training models with reinforcement learning, ideally for science, mathematics, code, or other complex-reasoning domains. You have shipped working RL systems that beat non-trivial baselines, with rigorous experimental methodology.
- Proficiency with modern ML frameworks and distributed training. You can move from a single GPU to a cluster without rewriting your code, and you understand what breaks at each scale.
- A physics or mathematics background providing intuition for physical reasoning and scientific tool use. You can hold a substantive conversation with a domain physicist.
Nice to Have