Member of Technical Staff, Product Engineering

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 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. We are seeking a Member of Technical Staff, Product to build every surface a human touches at PSI: the tools users run physics campaigns through, the SDK engineers build on, and the interfaces partners see. Everything else we build gets judged at this layer, by people who have somewhere else to be. The measure of the work is whether they came back. Role and Responsibilities Build the user's path through the platform: chat, SDK, campaign management, and the budget and spend tooling underneath. All of it thin clients over one contract, so what we build once shows up everywhere. Own time-to-first-result as a product metric. A new user should get from arriving to a real result with no engineer in the room. Every default, error message, and empty state either shortens that path or is in the way. Build partner-facing surfaces from the same contract as everything else. One product with many faces. The public site counts: owned as a product, shipped fast, treated as the first thing a candidate or a customer sees, because it is. Build the design system as infrastructure. A constrained component library that humans and AI agents both compose against, so agent-written front-end code comes out consistent the way a linter makes any generated code come out clean. What We're Looking For Five or more years building product, and you have shipped a developer surface people adopted without being told to: an SDK, an API, an internal tool. Adoption you can prove, not features you can list. Full-stack range: React and TypeScript on the front, Python behind. API and SDK design taste: ergonomics, sensible defaults, backward-compatible evolution, and error messages written for the person hitting them at midnight. You think in contracts, and you treat the error path as part of the product. Evidence you work backwards from the user's problem. You can sit with a user, watch where they stall, and ship the thing that removes the stall rather than the feature they asked for. Shipping velocity with judgment. Small team, every surface real. You cut scope rather than corners, and you can say which is which. Nice to Have Developer-tools or platform-product background. You have been measured on activation, retention, or time-to-first-result before. Design-system experience: a component library other teams shipped with, not a style guide they ignored. Built for scientific, technical, or data-heavy users, and comfortable rendering and manipulating quantitative results. Made a product surface legible to AI agents as well as humans: structured outputs, machine-readable errors, components an agent can compose. 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.