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Vulcan Elements

Applied Machine Learning Engineer

Research Triangle Park, NCmidAdded yesterday

About this role

Vulcan Elements seeks an Applied Machine Learning Engineer to design and build scalable data infrastructure supporting rare-earth magnet manufacturing operations. You'll architect data pipelines, select platforms, and enable analytics and AI capabilities from pilot scale to a 10,000-ton facility, with potential to lead a team as the company grows.

What you'll do

  • Design end-to-end data architecture spanning operational stores, ETL pipelines, and analytics/AI layers
  • Evaluate and select data platforms (lakehouse, ETL tools, databases) balancing scalability, compliance, and cost
  • Implement data models and pipelines that collect, contextualize, and deliver data to dependent teams and tools
  • Ensure designs meet CUI and ITAR compliance requirements for controlled data handling
  • Document architectural decisions with clear reasoning to guide future team expansion
  • Collaborate with cross-functional stakeholders to translate operational requirements into durable systems

What they're looking for

  • Data architecture and platform design
  • ETL pipeline development
  • Data lakehouse technologies
  • Cloud platform experience (AWS, Azure, or GCP)
  • Database design and optimization
  • Python or similar programming languages
  • Data security and compliance (ITAR, CUI)
  • Operational data systems
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Vulcan Elements

Vulcan Elements manufactures rare-earth magnets for defense and aerospace applications, with a focus on American production and advanced processing capabilities. The company is hiring materials engineers, process specialists, mechanical engineers, safety professionals, and network engineers to support manufacturing operations, facility expansion, and process optimization.

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Likely interview questions

  • Describe your experience designing data architectures from scratch. How did you approach scaling from pilot to production?
  • Walk us through a platform evaluation you've conducted for data infrastructure. What criteria were most important?
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