Design for Manufacturing and Computational Geometry Engineer

• 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.

• 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.

• 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.

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.