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.
What You'll Do
- 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
What We're Looking For
- 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
Bonus Points
- Experience training geometry-specific models
- CAD experience
- Manufacturing experience
Equal Opportunity
Layup is an equal-opportunity employer. All qualified applicants will be treated with respect and receive equal consideration for employment without regard to race, color, creed, religion, sex, gender identity, sexual orientation, national origin, disability, uniform service, Veteran status, age, or any other protected characteristic per federal, state, or local law, including those with a criminal history, in a manner consistent with the requirements of applicable state and local laws, including the CA Fair Chance Initiative for Hiring Ordinance.
ITAR Requirements
To conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here.
Skills Required
- Experience training custom ML models beyond basic labeling or simple fine-tuning
- Experience with advanced object detection and computer vision
- Experience working with large, parameter-rich datasets
- Experience with geometry-based or spatial modeling
- Data classification experience
- Experience training geometry-specific models
- CAD experience
- Manufacturing domain experience
What We Do
Our goal is to deliver composite parts faster than any other supplier in the market. We want to continually impress our customers with our speed, quality, and responsiveness to their needs.
Why Work With Us
We plan to achieve our goals by building and implementing technology that hasn’t yet been applied to the composites industry, including: automated customer quoting, automated tooling design, integrating software with hardware to reduce engineering and production workloads, and implementing latest developments in 3D printing.
