Physical Superintelligence is a stealth 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 engineers to build platform infrastructure at the intersection of computational science, AI systems, and software engineering.
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.
Embed with customers to apply AI and ML to hard, real-world physics problems. Build ML models, simulations, and digital twins that beat traditional engineering workflows on accuracy, speed, or both, pair them with agentic optimization loops, and ship results customers can actually run in production.
Build the demos and engagement-specific tooling that make PSI's physics-AI capabilities tangible. Working artifacts and live dashboards, not slide decks. Every engagement ends with something a customer can run and see results from.
Translate real-world data into clean inputs for ML and simulation pipelines: sensor telemetry, design documents, operational logs, public data feeds. Define schemas, build ingestion pipelines, make every engagement's data usable in days, not months.
Drive each engagement end-to-end: scoping, technical implementation, customer-facing communication, and harvest discipline. Every engagement either compounds a generalizable capability we can reuse across customers or it is killed at renewal.
Five or more years building ML systems in production with grounding in applied physics, computational science, or engineering, at companies or labs known for scientific rigor. You have written ML code that solved a real physics or engineering problem, not just synthetic benchmarks.
Strong physics literacy in at least one quantitative domain. You can read a domain paper, hold a technical conversation with a senior domain engineer, and reason about non-linear trade-offs between accuracy, speed, and extrapolation risk.
Demonstrated ability to build ML models, simulations, or digital twins for physics or engineering problems. You have shipped systems that held up under real-world distribution shift, not just on the training set.
Customer-facing engineering instincts. You can walk a real-world environment with operators, communicate technically with PhD researchers and pragmatically with practitioners in the same week, and ship working artifacts rather than slide decks.
PhD or master's in physics, applied physics, engineering, computational science, or a comparable quantitative discipline.
Hands-on experience with simulation tools (OpenFOAM, Ansys, COMSOL, or comparable) and the trade-offs between high-fidelity simulation and faster, learning-based alternatives.
Experience with digital twins, physics-informed neural networks, neural operators, or other domain-specific ML architectures.
Prior on-site customer engineering or field deployment experience.
We are engineering-led. Engineers own problems end-to-end, 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: engineers work with agentic coding tools daily, write specs that are legible to humans and agents alike, and lead with leverage.
This is an in-person role based in Boston or San Francisco, with regular travel to customer sites. 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.
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