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Physical Superintelligence

Physical Superintelligence

AI-driven physics discovery engine for breakthroughs

Member of Technical Staff - AI Research

Full-Time
No salary listed
Senior
PhD
Remote in USA+1 more

More locations: Boston, MA, USA

Hybrid

Remote candidates are considered case by case; the role is based in Boston.

About the job

Requirements
  • A PhD in machine learning, computer science, physics, mathematics, or a related quantitative field, with a track record of recent publications at top venues such as NeurIPS, ICML, ICLR, or comparable physics-ML venues.
  • A hands-on track record building agents and training models with reinforcement learning, ideally for science, mathematics, code, or other complex-reasoning domains.
  • Experience shipping working reinforcement learning systems that beat non-trivial baselines, with rigorous experimental methodology.
  • Proficiency with modern machine learning frameworks and distributed training.
  • Ability to scale machine learning workloads from a single graphics processing unit to a cluster without rewriting code, with an understanding of failure modes at each scale.
  • A physics or mathematics background providing intuition for physical reasoning and scientific tool use.
  • Ability to hold a substantive conversation with a domain physicist.
Responsibilities
  • Build and train artificial intelligence agents and training systems that learn to do physics.
  • Investigate how agents acquire physical reasoning, how to design action spaces for scientific tool use, how to structure rewards for long-horizon discovery tasks, and how training infrastructure scales without compromising scientific validity.
  • Design evaluation workflows and benchmarks for physics reasoning.
  • Distinguish genuine reasoning from pattern matching and benchmark gaming.
  • Build instrumentation that makes agent behavior interpretable rather than opaque.
  • Publish results that advance the field of artificial intelligence for science.
  • Develop training curricula, reward structures, and architectures for discovery tasks.
  • Iterate on research approaches based on practical results and share successful results at leading machine learning venues when aligned with the mission.
  • Collaborate with physicists who design verification harnesses and engineers who build training infrastructure.
  • Ship working systems end-to-end rather than isolated research artifacts.
  • Own work from specification through deployment and on-call support.
Desired Qualifications
  • Hands-on experience with modern reinforcement learning algorithms such as Proximal Policy Optimization, Soft Actor-Critic, MuZero, multi-agent self-play, search-augmented methods, or comparable approaches.
  • Deep fluency with PyTorch or JAX, plus distributed training through Ray, XLA, Accelerate, or comparable technologies.
  • Experience applying agents to simulators, scientific tools, games, or rigorous benchmark suites.
  • Open-source contributions, conference presentations, or shipped research artifacts adopted by the community.

About the company

Physical Superintelligence

Physical Superintelligence

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Physical Superintelligence builds an AI-powered discovery engine to accelerate breakthroughs in physics. Its core product, GDP (Get Physics Done), is an open-source agentic AI physicist that scopes problems, plans research, performs derivations, and verifies results to move ideas toward validated insights. PSI describes a vertically integrated factory for physical superintelligence that combines theorist-like reasoning, computational-physics validation, and experimental testing, guided by a shaped-charge model to focus on challenging physics targets. Its goal is to discover and commercialize significant physics breakthroughs while ensuring broad public benefit as a Public Benefit Corporation.

Company Size

1-10

Company Stage

Seed

Total Funding

$58M

Headquarters

Cambridge, Massachusetts

Founded

2025

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Simplify's Take

What believers are saying

  • PSI says live pilots already run on operating data-center infrastructure today.
  • Breakthrough Energy Ventures, SV Angel, and NVIDIA-linked angels validate the 2026 fundraising story.
  • The September 2026 Fermi analysis gave PSI a visible physics demonstration and marketing edge.

What critics are saying

  • PSI still names no customers, savings numbers, or audited deployments as of September 4, 2026.
  • The Alpha Centauri mission depends on 2029 launch economics that PSI's own report says miss budget.
  • If Emmy fails to prove repeatable cost savings within 12 months, investors lose the thesis.

What makes Physical Superintelligence unique

  • PSI launched September 1, 2026 with $58M from Breakthrough Energy Ventures.
  • It combines virtual physicists, simulation, and verification into Emmy for machine-scale research.
  • Its first commercial wedge targets AI data centers and orbital infrastructure, not abstract moonshots.

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Benefits

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Company Equity

Company News

StreetInsider
Sep 1st, 2026
PSI raises $58M to build AI physics lab discovering new laws of the universe

Physical Superintelligence (PSI) has launched with $58 million in seed funding to industrialise physics discovery using AI. The Cambridge, Massachusetts-based startup was founded by Matt Pines, Alex Klokus, and Dr Alexander Wissner-Gross. Breakthrough Energy Ventures led the round, joined by Dragon Global, Robot Ventures, Solari, Susa, SV Angel, Valkyrie, Balaji Srinivasan, Anthony Scaramucci, and individual investors from OpenAI, NVIDIA, SoftBank Energy, Oracle, Hugging Face, JUMP Capital, and the a16z Scout Fund. PSI aims to build an AI-native physics lab that creates higher-fidelity world models and discovers new physical laws. Its first commercial application is Emmy, a platform for optimising terrestrial and orbital data centres. The company is also the founding technical partner for the Fermi Explorer Mission to Alpha Centauri.

PR Newswire
Sep 1st, 2026
Introducing Physical Superintelligence: The World's Most Advanced Physics Lab, Staffed by Virtual Physicists to Discover New Laws of the Universe

/PRNewswire/ -- Matt Pines, Alex Klokus, and Dr. Alexander Wissner-Gross today launched Physical Superintelligence (PSI) to build the world's most advanced...

The Deep View
Sep 1st, 2026
Can AI help crack interstellar travel?

Can AI help crack interstellar travel? AI is best known for transforming coding. Now, one startup is making the case for physics. On Tuesday, the AI-native physics research lab, Physical Superintelligence (PSI), emerged from stealth with $58 million in seed funding, led by Breakthrough Energy. It is launching with two proofs of concept: * A productized piece of its core platform, Emmy * Joining as a founding technical partner for the Fermi Explorer Mission, a nonprofit organizing the first privately funded interstellar space mission and the first AI-planned probe to Alpha Centauri Emmy, named for renowned physicist Amalie Emmy Noether, combines PSI's reasoning engine, consisting of sovereign pre-trained and post-trained models, with a large curated inventory of simulations to tackle research problems at a pace much quicker than humans could, according to the company. Moreover, Emmy can reason through a problem, then test its conclusions until its findings are verifiable, as Matt Pines, co-founder and CEO, told The Deep View. "Our systems run research campaigns: they decompose a problem, generate candidate approaches, and test them against simulation, live measurement, or machine-checked proof," said Pines. "Nothing counts as a result until it survives a check that sits outside the model." Initially, a subset of Emmy's capabilities will be used for optimizing terrestrial and orbital AI data centers and factories. PSI has already signed commercial agreements and live pilot deployments on operating data center infrastructure today, according to Pines. The second prong of the launch is PSI's involvement in the Fermi Explorer mission, whose ultimate goal is to launch the first spacecraft to another star system, targeting Alpha Centauri, the closest star system to Earth. This initiative is a major undertaking because Alpha Centauri is roughly 4.37 light-years away, which would take about 80,000 years to reach from Earth at the speeds of current spacecraft. That makes it quite a feat of engineering to build a vessel capable of the journey. PSI has already claimed to have contributed to the mission by validating its physics and identifying a substantially more efficient trajectory within the mission's mass and budget constraints. The company is using this finding to demonstrate that a small team using AI-native physics could do the work typically required of a national laboratory. This reflects the company's broader mission to contribute to discoveries that are both commercially and scientifically valuable. "Fermi asked us to assess mission feasibility: the propulsion, trajectory, and power questions that determine whether the mission closes," said Pines. "Our technology ran the analysis, with our physicists directing the work. Fermi's technical team, which comes out of Starcloud, verified the analysis. The report was also written so the analysis can be rerun, and reproduction is the standard we want to be held to." PSI was founded by Pines, Alex Klokus, and Dr. Alexander D. Wissner-Gross, Ph.D, who combined to bring expertise across physics, economics, government, and tech. The broader team comprises physicists, AI researchers, experimenters, and builders, and PSI is actively hiring more talent. Interested applicants can apply online. Its deeper View. ChatGPT became the catalyst for the current AI boom, and since then, The Deep View has seen many companies try to compete by creating AI products. The result is that many of these products end up being repetitive or AI-washed offerings that have largely caused mainstream AI fatigue. However, some labs are developing focused, task-based AI solutions to solve big problems. Physical Superintelligence is a prime example, as it showcases just how instrumental AI can be as a catalyst to spur further innovation and development, even unlocking discoveries that have been very difficult to solve, with this extreme example of building a vessel capable of reaching Alpha Centauri. It's refreshing to see teams with ambitions this big.