AI and Computational Geometry Engineer

About the Role

Atomic Machines is building the Matter Compiler™, a digital manufacturing platform that produces complete, functional micromachines from bits and raw materials. This role owns the Design for Manufacturing (DFM) function as software within the Atomic Machines CAM stack. You will develop the algorithms, representations, and constraints that convert device geometry into manufacturable geometry under real process constraints—eliminating manual translation steps. The scope includes part arrangement on blanks, retention during processing, manufacturability constraints, and physical models that ground DFM rules in process mechanics. You will collaborate across AI, Modeling and Simulation, Design, and Process Engineering teams to bridge design intent and machine execution.

Responsibilities

• Build DFM as a software capability: algorithms and constraints that convert device geometry into executable process geometry, covering part arrangement, retention, and release.
• Encode manufacturability constraints into the design loop, ensuring infeasibility surfaces at design time rather than during fabrication.
• Translate process intuition into auditable, testable code by working directly with design and process engineers.
• Ground DFM decisions in physical process mechanics, collaborating with the Modeling and Simulation team.
• Validate layouts against fab runs, define correctness criteria, and refine constraints based on real-world failures.
• Develop a structured knowledge base from production history to support calibration, regression testing, and learned components.

Requirements

• Minimum of 5 years of relevant industry experience or a PhD in a related field (spanning early career to Staff/L4–L6).
• Practical DFM experience demonstrated by writing code that generates geometry under real manufacturing constraints (e.g., slicers, toolpath software, sheet metal stamping, PCB layout, or comparable design automation).
• Working computational geometry skills: 2D boolean operations, polygon offsetting, packing, and no-fit-polygon reasoning.
• Strong software engineering: proficiency in Python and a systems language, with experience driving geometry kernels/libraries (e.g., Shapely, Clipper, OpenCascade, CGAL) via APIs.
• Proven ability to tackle novel, poorly specified problems (e.g., PhD research, open-source contributions, patents, or greenfield industry work).
• Willingness to iterate based on physical evidence from the fab and collaborate with process engineers.
• Degree in Mechanical Engineering, Computer Science, Applied Math, Computational Design, or a related field (Bachelor’s, Master’s, or PhD).

Preferred Qualifications

• Exposure to laser micromachining or subtractive micro-scale processes (e.g., kerf, heat-affected zone, tabbing, part release).
• Mechanics background to reason about part stability during processing or interest in building this with the Modeling and Simulation team.
• Experience with combinatorial/geometric optimization (MILP, constraint programming, metaheuristics).
• Machine learning on geometric data (e.g., learned models over meshes/B-rep graphs, neural fields, or learning from expert demonstration).
• Experience integrating heuristic/learned components into deterministic, auditable pipelines with validation and fallback behavior.
• Familiarity with CAE tools (e.g., Comsol, Ansys, Abaqus).
• Contributions to open-source geometry or manufacturing software.

Pay

Salary range: $200,000–$250,000 USD. Compensation includes equity and benefits.