atomicmachines · Emeryville, or Santa Clara, California
Skills: Python
Atomic Machines is building the Matter Compiler™, the first in a new class of AI-native, all-digital manufacturing systems that build working machines directly from code. The Matter Compiler (MC) is aimed at micro-machines: motors and gears the size of a grain of sand, robots small enough to work inside the body, cooling built directly into the chips that run AI, and the many devices like them that have been waiting on a way to build them. Its first product is PrimeSwitch™, an electromechanical power relay the size of a button battery that carries 150 amps continuous current and opens in 50 microseconds, about a thousand times faster than a conventional contactor. Founded by Jeff Holden — serial entrepreneur, ex-Amazon SVP and Uber’s first Chief Product Officer — the company has raised $250 million to date. Atomic Machines is based in the San Francisco Bay Area, with offices in Emeryville and Santa Clara. Why This Role Matters CAM connects geometric intent with physical processes. You’ll build the software infrastructure that lets process experts express where an operation acts, what effect it should produce, and which constraints it must satisfy. The challenge is to turn that knowledge into reusable representations, algorithms and interfaces. These must extend beyond familiar etching or 3D-printing workflows to support different ways of shaping, treating and assembling materials. Success means new processes can build on shared geometric capabilities while preserving the assumptions and behavior that make each process distinct. You’ll bring strong software engineering and deep interest in geometric reasoning, working with experts who bring the physics and process knowledge. AI fluency is central: use agents throughout development while owning the decisions, code and results you accept. What You’ll Do Build and evolve Go and Python software infrastructure for representing geometric intent and generating physical-process operations. Develop reusable geometric represen…
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