{"id":34169,"url":"https://alion.io/job/vulcanelements-applied-machine-learning-engineer","title":"Applied Machine Learning Engineer","company":{"id":8041,"name":"Vulcan Elements","domain":"vulcanelements.com","url":"https://alion.io/company/vulcanelements","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":91,"open_postings":7,"ghost_share":0,"stale_share":0.143,"repost_share":0,"time_to_fill_p50_days":69,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Durham, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":125000,"max_usd":238000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1213},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Computer Vision","optional":true},{"name":"Time Series Forecasting","optional":true}],"status":"live","first_seen_at":"2026-07-23T17:44:02Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-17T19:26:27Z","board_verified":true,"closed_at":null,"days_open":86,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":86},"description":"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. \nAs 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. \nResponsibilities \nProblem Identification & Applied Analytics \nWork directly with manufacturing, engineering, quality, maintenance, supply chain, and business teams to identify problems where machine learning or advanced analytics could create meaningful value \nTranslate operational problems into clear technical requirements, analytical questions, and measurable outcomes \nEvaluate whether problems are best addressed through machine learning, statistical analysis, optimization, rules-based methods, or existing tools \nAnalyze manufacturing and business data to identify process drivers, operational trends, sources of variation, and opportunities to improve yield, quality, throughput, reliability, or cost \nSolutions & Product Development \nBuild dashboards, internal tools, and lightweight applications that address operational needs and support future machine learning use cases \nTurn analyses and model outputs into practical tools that can be used by technical and non-technical teams \nDevelop the supporting code and interfaces required to make those tools reliable and maintainable \nWork 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 \nMachine Learning & Technical Development \nDevelop and deploy machine learning and statistical models for use cases such as quality prediction, anomaly detection, forecasting, process optimization, maintenance, scheduling, and document analysis \nSelect modeling approaches appropriate for limited, noisy, changing, or highly correlated manufacturing data rather than defaulting to unnecessary complexity \nDesign validation methods that account for time, production batches, equipment, materials, recipes, and other sources of potential data leakage \nDeploy models into applications and operational workflows, and monitor their performance, reliability, and continued relevance over time \nClearly document model assumptions, limitations, uncertainty, dependencies, and intended use \nEvaluate external AI platforms, models, and vendors, distinguishing useful capabilities from immature or poorly matched technology \nHelp establish practical standards for AI and machine learning development, validation, deployment, monitoring, and governance across Vulcan \nResponsibilities and tasks outlined are not exhaustive and may change as determined by the needs of the business. \nQualifications \n5+ 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 \nStrong proficiency in Python and SQL, including experience writing maintainable production code \nDemonstrated ability to carry an ambiguous business or operational problem through data preparation, solution design, implementation, and deployment \nExperience building APIs, data products, or decision-support tools that are used by non-technical stakeholders \nStrong grounding in statistics, model evaluation, experimental design, and common supervised and unsupervised machine learning methods \nExperience working with incomplete, inconsistent, or operational data and making sound decisions despite imperfect information \nApplies sound software development practices including version control, testing, documentation, monitoring, and reproducible development \nStrong communicator who can work across technical and non-technical stakeholders and translate between operational problems and technical solutions \nMust be a U.S. Person due to required access to U.S. export-controlled information or facilities \nDesired Skills \nExperience in a manufacturing, industrial, laboratory, or operations-heavy environment \nFamiliarity with manufacturing concepts such as genealogy, recipes, work orders, process parameters, yield, scrap, quality holds, equipment states, and process capability \nExperience working with time-series, sensor, image, document, or other forms of manufacturing and laboratory data \nExperience deploying and monitoring machine learning models, including familiarity with experiment tracking, model versioning, and model-performance monitoring \nExperience with optimization, simulation, anomaly detection, computer vision, natural-language processing, or large language model applications \nFamiliarity with industrial data systems such as SCADA, historians, MES, ERP, CMMS, LIMS, and QMS \nExperience evaluating and integrating commercial AI platforms, model APIs, or open-source models \nExperience working in a controlled information environment; 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