Machine Learning Engineer

At Layup Parts, we're developing the technology that will build the future.
We're a manufacturing technology company replacing months of lead time with days, using proprietary software, automation, and advanced manufacturing systems built for speed. Our customers are inventing what's next, in aerospace, defense, robotics, and beyond. To keep up with them, manufacturing has to change. That's what we're building. We're looking for a Machine Learning Engineer to train custom models using our internal data. This work spans design generation, cost estimation, evaluating the complexity and difficulty of a given design, and extracting structured data out of existing documentation. We're looking for someone who has trained custom models on large, parameter-rich datasets, ideally with a geometric or spatial component, and who is energized by problems in that space specifically.

Train and iterate on custom ML models using Layup's internal manufacturing and design data Build models that estimate cost and predict design complexity or manufacturing difficulty from part geometry Develop models that generate or assist in generating new designs based on historical design data Build pipelines to extract structured data (specs, dimensions, material callouts, etc.) from existing engineering documents and drawings Evaluate and select modeling approaches suited to geometric, spatial, and other structured data, rather than text-based problems Work closely with engineering and manufacturing teams to source, clean, and label internal datasets Own model performance end-to-end, from data pipeline through training, evaluation, and deployment into internal tools Continuously identify new opportunities where custom models could improve design, estimation, or manufacturing workflows

Experience training custom models beyond basic labeling or fine-tuning workflows Experience with advanced object detection at minimum; data classification experience is a strong plus Experience with geometry-based modeling is highly preferred Experience working with large, parameter-rich datasets Strongest fit is someone whose background is in structured, spatial, or geometric data problems rather than natural language or LLM-centric work

Experience training geometry-specific models CAD experience Manufacturing experience

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