Generative AI CAD Engineer
Build generative AI-driven CAD pipelines to automate design of micromachines, using Python/Rust and CAD APIs to assemble and optimize 3D assemblies from parametric components.
- Compensation
- $210K–$260K / yr
- Location
- Emeryville, California
- Work setup
- Remote
- Seniority
- C level
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- Knowledge of AI-based CAD generation, generative models for 3D geometry, or generative design algorithms, demonstrated through coursework, research, publications, or substantial projects.
- Proficiency in interacting with CAD or geometry tooling via APIs (Python, Rust, or other relevant languages).
- Familiarity with CAD interoperability standards (STEP, IGES, JT, GLTF, or similar).
- Experience with NURBS and B-Rep.
- Bachelor's, Master's, or PhD degree in Computer Science, Machine Learning, Computational Design, Mechanical Engineering, Aerospace Engineering, Applied Math, or a related field. Recent graduates are w
Nice to have
- Strong background in programmatic CAD workflows or programmatic 3D geometry (e.g., with Onshape API, SolidWorks API, Fusion API, CadQuery, Build123d, FreeCAD, or similar).
- Experience with CAD manipulation and parametrization, including scripting and automation.
- Experience with geometric modeling kernels (e.g., OpenCascade, Parasolid).
- Experience with implicit geometric representations (SDFs, neural fields, occupancy networks).
- Experience with meshing, especially for simulation or manufacturing purposes.
- Experience with Model-Based Definitions.
- Experience with simulation-in-the-loop design, shape optimization, or topology optimization workflows.
- Contributions to open-source CAD, geometry, or 3D ML libraries.
Full description
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.
About this role
As a Generative AI CAD Engineer, you can shape our CAD/automation workflows by developing generative and programmatic CAD pipelines that transform design specifications into manufacturable micro-device assemblies. You'll collaborate closely with our AI, Modeling & Simulation, and Design engineers to build systems that automatically explore, assemble, and optimize designs from a library of components. You will join a tight cross-functional team dedicated to pushing the limits of computational design.
This position is onsite in our Emeryville, CA office, with openness to an occasional work-from-home arrangement when needed.
When referring to the compensation band below, keep in mind that it can vary depending on the candidate's experience and level. This posting is not associated with a specific level, but it spans Junior, Senior, and Staff levels, i.e. L4 to L6. The level will be defined upon application. We actively encourage recent graduates, PhDs, and postdocs to apply. A strong research record can substitute for industry experience, and we will consider strong Junior candidates who are excited to grow into a leadership role.
What You'll Do:
- Develop GenAI-driven workflows for CAD generation and automated design exploration.
- Build CAD workflows that programmatically construct, modify, and evaluate 3D assemblies.
- Create and maintain a library of parametric components for generative design exploration and training.
- Curate and parse large sets of CAD models with product, manufacturing, and tolerance information into both standardized APIs and batchable datasets for training generative models.
What You'll Need:
Required
- Knowledge of AI-based CAD generation, generative models for 3D geometry, or generative design algorithms, demonstrated through coursework, research, publications, or substantial projects.