Design for Manufacturing and Computational Geometry Engineer
Owns the design-for-manufacturing (DFM) layer of Atomic Machines' CAM stack, writing algorithms that convert device geometry into manufacturable geometry under real process constraints. Day-to-day work spans computational geometry (layout, fixturing, offsetting), DFM rules, and physical grounding, in Python plus a systems language and geometry kernels like Shapely and CGAL.
- Compensation
- $200K–$250K / yr
- Location
- Emeryville, California
- Work setup
- Not specified
- Seniority
- C level
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Design
- Posted
Skills
Must have
- Minimum 5 years relevant industry experience or a PhD in a related field
- Practical DFM experience writing code that generates geometry under real manufacturing constraints (e.g., slicer/toolpath software, sheet metal stamping design, PCB/lead frame layout)
- Working computational geometry ability: 2D boolean operations, polygon offsetting, packing and no-fit-polygon style reasoning
- Strong software engineering: Python plus a systems language
- Comfort driving geometry kernels and libraries through their APIs (Shapely, Clipper, OpenCascade, CGAL, or similar)
- Demonstrated productivity on novel, poorly specified problems (PhD, open source contributions, patents, or greenfield industry work)
- Willingness to ground work in physical evidence from the fab and iterate with process engineers
- Bachelor's, Master's, or PhD in Mechanical Engineering, Computer Science, Applied Math, Computational Design, or a related field
Nice to have
- Exposure to laser micromachining or other subtractive micro-scale processes (kerf, heat-affected zone, tabbing, part release)
- Enough mechanics background to reason about part stability during processing
- Combinatorial and geometric optimization using MILP, constraint programming, or metaheuristics
- Machine learning on geometric data (learned models over meshes or B-rep graphs, neural fields, learning from expert demonstration)
- Experience placing heuristic or learned components inside a deterministic, auditable pipeline
- Familiarity with CAE tools (e.g., Comsol, Ansys, Abaqus)
- Contributions to open-source geometry or manufacturing software
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 The Role:
The Matter Compiler™ will take a device design and produce a physical part without the manual translation steps. Design for manufacturing (DFM) is where that translation happens, and it is currently human work: an engineer reasons about how a part must be arranged, held, and processed, and encodes that judgment one design at a time.
This role owns that reasoning as software. It is the DFM function inside the Atomic Machines CAM stack, the engineering discipline of turning device geometry into manufacturable geometry under real process constraints, generally rather than case by case.
The scope is the full DFM layer: the geometry between a device model, the workpiece, and the machine processes, including how parts are arranged on a blank and held in place during cutting; the DFM rules for each process and material the platform supports; the constraints and checks that tell a designer a part cannot be made as drawn; and the physical models that ground those rules in what the processes do to the part.
The engineer in this role is the person on the team who thinks in manufacturing constraints and writes code that respects them. Design engineers bring geometry that cannot yet be built. Process engineers bring results from the machine that the rules did not predict. The person in this role connects those two and works inside a cross-functional team spanning AI, Modeling and Simulation, Design, and Process Engineering.
What You’ll Do:
- DFM as a software capability. The algorithms, representations, and constraints that convert device geometry into geometry a process can execute. This covers part arrangement on a blank, retention during processing, and release afterward, and it expands as we add processes.
- Manufacturability constraints in the design loop. Encoding what our processes can and cannot do, so infeasibility surfaces at design time rather than at the machine.