PSI Fellow

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. The PSI Fellowship is designed for exceptional physicists who want to work at the frontier of AI-accelerated scientific discovery for 3 to 6 months full-time, with the goal of producing substantive technical contributions and, where appropriate, publishable research.

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.

Collaborate directly with PSI's founding team and technical leadership on high-impact research at the intersection of physics and AI.

Work on projects aligned with your expertise, such as AI-for-physics benchmarks, physics discovery agents, verification and interpretability, post-training for scientific reasoning, or human-AI scientific collaboration.

Produce substantive contributions to PSI's technical roadmap and, where appropriate, publishable research that advances AI for science.

Use PSI's compute and tooling to ship working systems, not isolated research artifacts. Exceptional performers may have the option to convert to a full-time role, though not guaranteed.

Physics PhD or advanced PhD candidate (all but dissertation), with deep domain expertise in at least one physics field.

Demonstrated hands-on experience with modern AI and ML, not just curiosity. You have built things, shipped models, or contributed to working systems.

Strong programming skills in Python; experience with deep learning frameworks preferred. You can write production-quality code, not just notebooks.

Available full-time for 3 to 6 months and excited by the possibility that AI could fundamentally transform how physics is done.

Experience with LLM fine-tuning, RLHF, or other post-training methods.

Background in agentic AI systems or multi-step reasoning.

Published ML research or significant open-source contributions.

Track record of crossing disciplinary boundaries between physics, ML, and software engineering.

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.