> Markdown version of [/jobs/ext/4322060-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/4322060-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Engineer

- **Company:** Layup Parts
- **Location:** Huntington Beach, CA, United States
- **Contract:** Permanent contract
- **Skills:** Computer-Aided Design, Machine Learning, Object Detection, Data Classification, Delivery Pipeline, Large Language Models, Data Pipelines
- **Published:** October 11, 2026
- **Apply:** https://www.juju.com/job/16_fef7fd722

## About the Role

* Be awesome! Be the kind of person you want to work with!
 * 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

## Description

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