Applied Machine Learning Engineer

<p><span data-contrast="auto">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.</span><span data-ccp-props="{"335559739":180}"> </span></p> <p><span data-contrast="auto">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.</span><span data-ccp-props="{"335559739":240}"> </span></p> <p><strong><span data-contrast="none"><span data-ccp-parastyle="heading 2">Responsibilities</span></span></strong><span data-ccp-props="{"335559738":240,"335559739":120,"335572079":4,"335572080":4,"335572081":13421772,"469789806":"single"}"> </span></p> <p><strong><span data-contrast="auto">Problem Identification & Applied Analytics</span></strong><span data-ccp-props="{"335559738":180,"335559739":100}"> </span></p> <ul> <li><span data-contrast="auto">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</span><span data-ccp-props="{"335559739":60}"> </span></li> <li><span data-contrast="auto">Translate operational problems into clear technical requirements, analytical questions, and measurable outcomes</span><span data-ccp-props="{"335559739":60}"> </span></li> <li><span data-contrast="auto">Evaluate whether problems are best addressed through machine learning, statistical analysis, optimization, rules-based methods, or existing tools</span><span data-ccp-props="{"335559739":60}"> </span></li> <li><span data-contrast="auto">Analyze manufacturing and business data to identify process drivers, operational trends, sources of variation, and opportunities to improve yield, quality, throughput, reliability, or cost</span><span data-ccp-props="{"335559739":60}"> </span></li> </ul> <p><strong><span data-contrast="auto">Solutions & Product Development</span></strong><span data-ccp-props="{"335559738":180,"335559739":100}"> </span></p> <ul> <li><span data-contrast="auto">Build dashboards, internal tools, and lightweight applications that address operational needs and support future machine learning use cases</span><span data-ccp-props="{"335559739":60}"> </span></li> <li><span data-contrast="auto">Turn analyses and model outputs into practical tools that can be used by technical and non-technical teams</span><span data-ccp-props="{"335559739":60}"> </span></li> <li><span data-contrast="auto">Develop the supporting code and interfaces required to make those tools reliable and maintainable</span><span data-ccp-props="{"335559739":60}"> </span></li> <li><span data-contrast="auto">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</span><span data-ccp-props="{"335559739":60}"> </span></li> </ul> <p><strong><span data-contrast="auto">Machine Learning & Technical Development</span></strong><span data-ccp-props="{"335559738":180,"335559739":100}"> </span></p> <ul> <li><span data-contrast="auto">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</span><span data-ccp-props="{"335559739":60}"> </span></li> <li><span data-contrast="auto">Select modeling approaches appropriate for limited, noisy, changing, or highly correlated manufacturing data rather than defaulting to unnecessary complexity</span><span data-ccp-props="{"335559739":60}"> </span></li> <li><span data-contrast="auto">Design validation methods that account for time, production batches, equipment, materials, recipes, and other sources of potential data leakage</span><span data-ccp-props="{"335559739":60}"> </span></li> <li><span data-contrast="auto">Deploy models into applications and operational workflows, and monitor their performance, reliability, and continued relevance over time</span><span data-ccp-props="{"335559739":60}"> </span></li> <li><span data-contrast="auto">Clearly document model assumptions, limitations, uncertainty, dependencies, and intended use</span><span data-ccp-props="{"335559739":60}"> </span></li> <li><span data-contrast="auto">Evaluate external AI platforms