AI and Computational Geometry Engineer

Responsibilities

• Own design-for-manufacturing (DFM) software within the CAM stack, converting device geometry into manufacturable geometry under real process constraints.
• Develop algorithms, representations, and constraints for part arrangement on a blank, retention during processing, and release afterward.
• Encode manufacturability constraints so infeasibility is identified during design rather than at the machine.
• Work with design and process engineers to formalize manufacturing judgment into auditable, testable software.
• Ground DFM decisions in physical process mechanics in collaboration with Modeling and Simulation.
• Define validation criteria for layouts, test against fabrication runs, and incorporate failures into constraints and models.
• Structure production history for calibration, regression testing, and future learned components.

Requirements

• At least 5 years of relevant industry experience or a PhD in a related field.
• Practical DFM experience writing code that generates geometry under real manufacturing constraints.
• Working knowledge of computational geometry, including 2D Boolean operations, polygon offsetting, packing, and no-fit-polygon reasoning.
• Strong software engineering skills with Python and a systems programming language.
• Experience driving geometry kernels and libraries through APIs.
• Demonstrated ability to work productively on novel, poorly specified problems.
• Willingness to ground work in physical evidence from fabrication and iterate with process engineers.
• Bachelor's, master's, or PhD in Mechanical Engineering, Computer Science, Applied Mathematics, Computational Design, or a related field.

Nice-to-haves

• Experience with laser micromachining or other subtractive microscale processes, including kerf, heat-affected zones, tabbing, and part release.
• Mechanics knowledge relevant to part stability during processing.
• Experience with combinatorial and geometric optimization using MILP, constraint programming, or metaheuristics.
• Machine learning on geometric data, including meshes, B-rep graphs, neural fields, or learning from expert demonstrations.
• Experience integrating heuristic or learned components into deterministic, auditable pipelines with validation and fallback behavior.
• Familiarity with CAE tools such as COMSOL, Ansys, or Abaqus.
• Contributions to open-source geometry or manufacturing software.

Compensation and Benefits

• Salary range: $200,000–$250,000 USD.
• Equity and benefits included.