Member of Technical Staff, Software 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

• Three or more years building product full stack: React and TypeScript in front, Python services behind. You have shipped surfaces real users depended on daily, and you have owned one from design through its first rough weeks in production.
• Three or more years building for users you worked with directly. You work backwards from where they stall, the things you ship get adopted, and the adoption is something you can prove with numbers rather than features you can list.
• Depth in one of the following. We hire for one lane and expect you to learn the others on our platform:
• Developer surfaces: SDKs, APIs, CLIs, and internal tools, with defaults, contracts, and error messages treated as the product.
• Frontend and design systems: polished user-facing product, and component libraries other teams ship with.
• Scientific data visualization: dashboards, notebooks, and quantitative rendering for technical and data-heavy users.
• Agent-legible surfaces: structured outputs, machine-readable errors, components AI agents compose as readily as humans do.

Nice to Have

• A second lane from the list above.
• Developer-tools or platform-product background: you have been measured on activation, retention, or time-to-first-result.
• API and SDK design taste: ergonomics, sensible defaults, backward-compatible evolution, and the error path treated as part of the product.
• Shipping velocity with judgment: you cut scope rather than corners, and you can say which is which.

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 hybrid, three days a week in person in our Cambridge, MA office. 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.