Vulcan Elements is manufacturing American rare-earth permanent magnets for a secure, resilient future. With a focus on national security and economic resiliency, we serve critical industries such as defense, aerospace, and automotive, powering a high-technology future. Vulcan Elements is building a team of ambitious professionals committed to Mission Focus, Technical Excellence, and Integrity.  As the Applied Machine Learning Engineer, you will identify and develop machine learning solutions that improve how Vulcan manufactures products and operates its business. You will work from problem identification through deployment: understanding operational workflows, determining whether machine learning is appropriate, preparing data for analysis, developing models, and integrating results into the tools and decisions that depend on them. You will collaborate closely with operations, engineering, quality, business teams, and data engineers to turn operational problems into practical, measurable solutions.  Responsibilities  Problem Identification & Applied Analytics  <ul> <li>Work directly with manufacturing, engineering, quality, maintenance, supply chain, and business teams to identify problems where machine learning or advanced analytics could create meaningful value </li> <li>Translate operational problems into clear technical requirements, analytical questions, and measurable outcomes </li> <li>Evaluate whether problems are best addressed through machine learning, statistical analysis, optimization, rules-based methods, or existing tools </li> <li>Analyze manufacturing and business data to identify process drivers, operational trends, sources of variation, and opportunities to improve yield, quality, throughput, reliability, or cost </li> </ul> Solutions & Product Development  <ul> <li>Build dashboards, internal tools, and lightweight applications that address operational needs and support future machine learning use cases </li> <li>Turn analyses and model outputs into practical tools that can be used by technical and non-technical teams </li> <li>Develop the supporting code and interfaces required to make those tools reliable and maintainable </li> <li>Work with the Data Engineer, IT, and OT teams to define data requirements, resolve data quality issues, and establish reliable paths from source systems into deployed applications </li> </ul> Machine Learning & Technical Development  <ul> <li>Develop and deploy machine learning and statistical models for use cases such as quality prediction, anomaly detection, forecasting, process optimization, maintenance, scheduling, and document analysis </li> <li>Select modeling approaches appropriate for limited, noisy, changing, or highly correlated manufacturing data rather than defaulting to unnecessary complexity </li> <li>Design validation methods that account for time, production batches, equipment, materials, recipes, and other sources of potential data leakage </li> <li>Deploy models into applications and operational workflows, and monitor their performance, reliability, and continued relevance over time </li> <li>Clearly document model assumptions, limitations, uncertainty, dependencies, and intended use </li> <li>Evaluate external AI platforms, models, and vendors, distinguishing useful capabilities from immature or poorly matched technology </li> <li>Help establish practical standards for AI and machine learning development, validation, deployment, monitoring, and governance across Vulcan </li> </ul> Responsibilities and tasks outlined are not exhaustive and may change as determined by the needs of the business.  Qualifications  <ul> <li>5+ years of experience in machine learning, applied data science, software engineering, data engineering, or a closely related technical role, with a track record of building and deploying production systems </li> <li>Strong proficiency in Python and SQL, including experience writing maintainable production code </li> <li>Demonstrated ability to carry an ambiguous business or operational problem through data preparation, solution design, implementation, and deployment </li> <li>Experience building APIs, data products, or decision-support tools that are used by non-technical stakeholders </li> <li>Strong grounding in statistics, model evaluation, experimental design, and common supervised and unsupervised machine learning methods </li> <li>Experience working with incomplete, inconsistent, or operational data and making sound decisions despite imperfect information </li> <li>Applies sound software development practices including version control, testing, documentation, monitoring, and reproducible development </li> <li>Strong communicator who can work across technical and non-technical stakeholders and translate between operational problems and technical solutions </li> </ul> Must be a U.S. Person due to required access to U.S. export-controlled information or facilities  Desired Skills  <ul> <li>Experience in a manufacturing, industrial, laboratory, or operations-heavy environment </li> <li>Familiarity with manufacturing concepts such as genealogy, recipes, work orders, process parameters, yield, scrap, quality holds, equipment states, and process capability </li> <li>Experience working with time-series, sensor, image, document, or other forms of manufacturing and laboratory data </li> <li>Experience deploying and monitoring machine learning models, including familiarity with experiment tracking, model versioning, and model-performance monitoring </li> <li>Experience with optimization, simulation, anomaly detection, computer vision, natural-language processing, or large language model applications </li> <li>Familiarity with industrial data systems such as SCADA, historians, MES, ERP, CMMS, LIMS, and QMS </li> <li>Experience evaluating and integrating commercial AI platforms, model APIs, or open-source models </li> <li>Experience working in a controlled information environment; familiarity with the handling requirements for Controlled Unclassified Information (CUI) or export-controlled technical data under ITAR or EAR </li> </ul>

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