Staff AI Researcher: Agents & Training Systems for Physics

Physical Superintelligence; Boston (MA); Hybrid; USD 150,000 - 230,000; Full time

Benefits offered by this job

Equity

Job summary

Physical Superintelligence in Boston is building AI systems to discover new physics at scale. We are seeking researchers to develop agents and training systems that learn to do physics, and to publish results that advance our mission.

The role emphasizes shipping working systems, deep expertise in reinforcement learning, model architectures, and verification. We welcome collaborations with physicists and engineers, and remote candidates may be considered on a case-by-case basis.

Qualifications

• Three or more years of research experience after a PhD or five or more after a master's, with recent publications at top venues.
• Three or more years of hands-on research engineering with the modern ML stack: PyTorch or JAX, training and post-training frameworks, inference engines for rollouts, experiments run on multi-GPU clusters.
• Depth in at least one of the following. We hire for one lane and expect you to work across the others: reinforcement learning, verification, modeling, architectures.

Responsibilities

• Do original research on agents and training systems that learn to do physics, and publish it where it serves the mission.
• Build what you research: training runs, evaluations and harnesses that ship as working systems, not isolated artifacts.
• Go deep in one lane: reinforcement learning and post-training; agents, evaluation and verification; model architectures and training dynamics; scientific and generative modeling.
• Work with the physicists who design verification harnesses and the engineers who build training infrastructure.

Skills

Research experience

Hands-on ML

Multi-GPU training

Deep learning

Education

PhD or MSc in a relevant field

Tools

PyTorch

JAX

Inference engines

Training frameworks

Job description

Physical Superintelligence in Boston is building AI systems to discover new physics at scale. We are seeking researchers to develop agents and training systems that learn to do physics, and to publish results that advance our mission.

The role emphasizes shipping working systems, deep expertise in reinforcement learning, model architectures, and verification. We welcome collaborations with physicists and engineers, and remote candidates may be considered on a case-by-case basis.