Member of Technical Staff, AI Research

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 AI researchers to build the agents and training systems that learn to do physics.
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.
WHAT 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
- Hands-on experience with modern RL algorithms (PPO, SAC, MuZero, multi-agent self-play, search-augmented methods, or comparable).
- Deep fluency with PyTorch or JAX, plus distributed training via Ray, XLA, Accelerate, or comparable.
- Experience applying agents to simulators, scientific tools, games, or rigorous benchmark suites.
- Open-source contributions, conference presentations, or shipped research artifacts that the community has adopted.
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 technical breadth, systems thinking, scientific curiosity, and shipping velocity. We are an equal opportunity employer and value diverse perspectives in building platforms for AI-driven discovery.

Employment type
FullTime

Work arrangement
Yes