Machine Learning Engineer at Layup Parts
Huntington Beach, CA
$120,000–$185,000
Aug 3, 2026
Huntington Beach, CA
Sep 24, 2026
What this job asks for AI summary
A manufacturing technology startup is hiring an ML engineer to build and own custom models trained on internal design and manufacturing data. The work centers on geometry- and spatial-data problems — cost estimation, design complexity prediction, design generation, and structured data extraction from engineering documents — rather than NLP or LLM work. The role spans the full lifecycle from data pipeline through training, evaluation, and deployment into internal tooling.
Senior level · Full-time
Pay in the description: $120,000–$185,000
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How this req sits in the market our data
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Aug 5, 2026. It is a model, not a headcount.
Why we read it this way (6)
The posting names no specific ML frameworks (e.g. PyTorch, TensorFlow), programming languages, or cloud platforms — the skills list reflects only what the JD concretely names.
Custom ML model training and object detection are stated as hard requirements ('at minimum'); data classification is called 'a strong plus' and geometry-based modeling is 'highly preferred' — both treated as preferred.
CAD and manufacturing experience appear under 'Bonus Points' and are treated as preferred.
The job location is not stated explicitly; the company (Layup Parts) is based in the Los Angeles area but the posting does not confirm a specific work location or remote policy — no CBSA has been set.
ITAR compliance is required (US persons only), but no formal US government security clearance is gated.
Ignored 4 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): custom ML model training, geometry-based modeling, large-scale dataset handling, model evaluation and deployment.
Read the full posting
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