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Open nowPosted 77 days ago

Design for Manufacturing and Computational Geometry Engineer

Atomic Machines27 open roles

Pay
$200,000 – $250,000 a year
Where
Emeryville, California
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Your applicationOpen nowDesign for Manufacturing and Computational Geometry EngineerAtomic Machines · Emeryville, California

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The clock on this job

Early applications get read.

13.3% of postings close within 7 days. Measured by our own scanner across the market. Atomic Machines postings stay open a median of 52 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.6%3 days
  3. 13.3%7 days
  4. 21.3%14 days
  5. 35.7%30 days
  6. 59.1%60 days
This job: posted 77 days ago

Atomic Machines median: 52 days open

The posting

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.
  • The bridge between process intuition and code. Working directly with design and process engineers to elicit the judgment they apply by hand, formalize it, and make it auditable and testable.
  • Physical grounding. Moving DFM decisions from heuristics toward criteria based on the mechanics of the process, with our Modeling and Simulation team.
  • Validation against reality. Defining what correct means for a layout, testing against fab runs, and folding failures back into the constraints and models.
  • The knowledge base. Turning our production history into a structured record that supports calibration, regression testing, and eventually learned components.

What You’ll Need:

  • This posting is not tied to a specific level and spans early career through Staff, or L4 to L6. Candidates should have a minimum of 5 years of relevant industry experience or a PhD in a related field.
  • Practical DFM experience, demonstrated by work where you wrote code that generates geometry under real manufacturing constraints. Relevant examples include slicer or toolpath software for additive manufacturing, non-standard toolpathing strategies such as continuously self-supporting structures, design software for sheet metal stamping or other tool and die applications, PCB or lead frame layout, or comparable design automation work where geometry is constrained by physics rather than by convention.
  • Working computational geometry ability: 2D boolean operations, polygon offsetting, packing and no-fit-polygon style reasoning.
  • Strong software engineering: Python plus a systems language, and comfort driving geometry kernels and libraries through their APIs (Shapely, Clipper, OpenCascade, CGAL, or similar).
  • A clear demonstration of working productively on novel, poorly specified problems. A PhD is one way to show this. Open source contributions, patents, or industry work on greenfield problems count equally.
  • Willingness to ground your work in physical evidence from the fab, and to iterate with the engineers running the process.
  • Bachelor's, Master's, or PhD in Mechanical Engineering, Computer Science, Applied Math, Computational Design, or a related field.

Bonus Points For:

  • 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, or the interest to build that with our Modeling and Simulation team.
  • Combinatorial and geometric optimization, using MILP, constraint programming, or metaheuristics.
  • Machine learning on geometric data, for example learned models over meshes or B-rep graphs, neural fields, or learning from expert demonstration. This is a growth direction for the role, not an entry requirement.
  • Experience placing heuristic or learned components inside a deterministic, auditable pipeline, including validation and fallback behavior.
  • Familiarity with CAE tools (e.g., Comsol, Ansys, Abaqus).
  • Contributions to open-source geometry or manufacturing software.

The compensation for this position also includes equity and benefits.

Salary Range

$200,000—$250,000 USD

Atomic Machines is an equal opportunity employer that values diversity and does not discriminate based on characteristics protected by applicable law. We are committed to an inclusive interview experience, including for individuals with disabilities. To request an accommodation, contact [email protected]. To help our recruiting team manage application volume and respond to candidates more efficiently, Atomic Machines may use AI-assisted tools.

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