Machine Learning Engineer

Layup Parts•Huntington Beach, CA

7d•$120,000 - $185,000•Onsite

About The Position

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.

Requirements

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

Nice To Haves

Experience training geometry-specific models
CAD experience
Manufacturing experience

Responsibilities

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