Research Scientist, AI

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 AI researchers to develop reinforcement learning agents and training systems for scientific discovery.

Role and Responsibilities

Build and train AI systems for physics discovery, working with physicists who design verification harnesses and engineers who build training infrastructure. Focus on core AI research questions including how agents learn physics reasoning, action space design for scientific discovery, reward structure development, and training systems that scale.

Build and train reinforcement learning agents using modern approaches including PPO, SAC, MuZero, and multi-agent self-play and other methods

Design agent architectures for physics reasoning and scientific tool use

Implement training curricula and reward structures for discovery tasks

Develop evaluation workflows and benchmarks for physics reasoning capabilities

Build instrumentation to understand agent behavior and learning dynamics

Collaborate with physicists and engineers on system design and architecture

What We're Looking For

We seek candidates with experience building agents and training models with reinforcement learning. You should have proficiency in modern machine learning frameworks and understand distributed training systems with a track record shipping working AI systems.

Core AI and machine learning skills:

Hands-on experience with modern reinforcement learning algorithms including PPO, SAC, MuZero, and multi-agent self-play and other methods

Proficiency with PyTorch or JAX, distributed training using Ray, XLA, or Accelerate, and modern pretraining workflows

Valued backgrounds and experience:

Physics or mathematics background providing intuition for physical reasoning and mathematical modeling

Experience applying agents to simulators, games, scientific tool use, or benchmark design with rigorous experimental methodology

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 AI research depth, scientific curiosity, and ability to ship working systems. We are an equal opportunity employer and value diverse perspectives in building AI for scientific discovery.