{"schemaVersion":"jobsearcher.job.v1","id":"4784c3a1d89b516180d2ecb7","url":"https://jobsearcher.com/jobs/4784c3a1d89b516180d2ecb7","canonicalUrl":"https://jobsearcher.com/jobs/4784c3a1d89b516180d2ecb7","title":"Senior Applied Machine Learning Engineer","description":"Location: Remote (Continental US)\nDepartment: Product & Engineering\nClearance: U.S. Citizen, Public trust eligible\nNice to have: VA Cleared\nRole Overview\nWe are seeking a senior-level engineer to design and deliver intelligent automation capabilities for large-scale document review workflows governed by strict privacy and compliance requirements. This role focuses on applying machine learning, computer vision, and natural language techniques to understand, classify, and transform complex document sets within an established data and processing pipeline. This role includes large-scale text analytics and document intelligence across millions of scanned pages. You will design systems to identify structural and semantic similarities, group related documents, detect recurring content patterns, and surface trends across similar document collections. The work requires building scalable clustering, embedding, and pattern-discovery pipelines that transform unstructured and semi-structured page-level data into actionable insights.\nYou will work hands-on with high-volume, heterogeneous document collections to improve accuracy, consistency, and throughput of privacy-driven review processes. The work emphasizes practical outcomes, explainability, and operational reliability over experimental or research-only approaches.\nKey Responsibilities\nAnalyze large document collection to detect trends, anomalies, and emergent patterns that inform automation strategy and compliance workflows.\nDesign and implement machine learning solutions that analyze scanned and born-digital documents to support automated privacy and compliance decisions\nLeverage existing structured signals (such as spatial coordinates, annotations, and metadata) to discover repeatable document patterns and reusable automation opportunities\nDevelop classification and detection models that improve document understanding, including content type recognition and metadata validation\nApply computer vision techniques to locate visually significant elements within documents, such as signatures, forms, images, or sensitive regions\nBuild and tune text-based models to identify and interpret sensitive information across diverse document formats and quality levels\nCreate decisioning logic that distinguishes between documents requiring review and those eligible for automated pass-through\nIntegrate models and services into production-grade pipelines with attention to performance, scale, auditability, and failure handling\nCollaborate closely with platform, data, and domain experts to ensure solutions align with operational realities and regulatory expectations\nMonitor, evaluate, and continuously improve model performance using real-world feedback and downstream outcomes\nRequired Skills & Experience\nStrong background in applied machine learning with real-world production deployments\nHands-on experience with document analysis, document classification, or document understanding systems\nProficiency in computer vision techniques for layout analysis, object detection, and region identification\nSolid grounding in text processing and natural language techniques for entity detection, pattern recognition, and content classification\nExperience working with noisy or imperfect inputs such as scanned documents, OCR output, or legacy data\nAbility to translate ambiguous business or compliance requirements into measurable technical outcomes\nStrong software engineering skills, including versioned deployments, testing, and maintainability of ML systems\nComfort working with large datasets and iterating on models based on empirical results rather than theoretical perfection\nPreferred Qualifications\nExperience in regulated or compliance-driven domains (privacy, legal, healthcare, government, or similar)\nFamiliarity with spatial or layout-aware document representations\nExperience building systems that support explainability, traceability, and audit requirements\nExposure to hybrid approaches that combine rules, heuristics, and learned models\nAbility to reason about tradeoffs between precision, recall, automation rate, and operational risk\nJob Type: Full-time\nPay: $130,000.00 - $170,000.00 per year\nBenefits:\n401(k)\n401(k) matching\nDental insurance\nHealth insurance\nLife insurance\nPaid time off\nProfessional development assistance\nVision insurance\nWork Location: Remote","company":"Iconect Development","rawCompany":"iconect development","city":"Remote","state":"OR","isRemote":false,"isActive":false,"createdAt":"2026-04-14T11:14:26.851Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Applied Machine Learning Engineer","description":"Location: Remote (Continental US)\nDepartment: Product & Engineering\nClearance: U.S. Citizen, Public trust eligible\nNice to have: VA Cleared\nRole Overview\nWe are seeking a senior-level engineer to design and deliver intelligent automation capabilities for large-scale document review workflows governed by strict privacy and compliance requirements. This role focuses on applying machine learning, computer vision, and natural language techniques to understand, classify, and transform complex document sets within an established data and processing pipeline. This role includes large-scale text analytics and document intelligence across millions of scanned pages. You will design systems to identify structural and semantic similarities, group related documents, detect recurring content patterns, and surface trends across similar document collections. The work requires building scalable clustering, embedding, and pattern-discovery pipelines that transform unstructured and semi-structured page-level data into actionable insights.\nYou will work hands-on with high-volume, heterogeneous document collections to improve accuracy, consistency, and throughput of privacy-driven review processes. The work emphasizes practical outcomes, explainability, and operational reliability over experimental or research-only approaches.\nKey Responsibilities\nAnalyze large document collection to detect trends, anomalies, and emergent patterns that inform automation strategy and compliance workflows.\nDesign and implement machine learning solutions that analyze scanned and born-digital documents to support automated privacy and compliance decisions\nLeverage existing structured signals (such as spatial coordinates, annotations, and metadata) to discover repeatable document patterns and reusable automation opportunities\nDevelop classification and detection models that improve document understanding, including content type recognition and metadata validation\nApply computer vision techniques to locate visually significant elements within documents, such as signatures, forms, images, or sensitive regions\nBuild and tune text-based models to identify and interpret sensitive information across diverse document formats and quality levels\nCreate decisioning logic that distinguishes between documents requiring review and those eligible for automated pass-through\nIntegrate models and services into production-grade pipelines with attention to performance, scale, auditability, and failure handling\nCollaborate closely with platform, data, and domain experts to ensure solutions align with operational realities and regulatory expectations\nMonitor, evaluate, and continuously improve model performance using real-world feedback and downstream outcomes\nRequired Skills & Experience\nStrong background in applied machine learning with real-world production deployments\nHands-on experience with document analysis, document classification, or document understanding systems\nProficiency in computer vision techniques for layout analysis, object detection, and region identification\nSolid grounding in text processing and natural language techniques for entity detection, pattern recognition, and content classification\nExperience working with noisy or imperfect inputs such as scanned documents, OCR output, or legacy data\nAbility to translate ambiguous business or compliance requirements into measurable technical outcomes\nStrong software engineering skills, including versioned deployments, testing, and maintainability of ML systems\nComfort working with large datasets and iterating on models based on empirical results rather than theoretical perfection\nPreferred Qualifications\nExperience in regulated or compliance-driven domains (privacy, legal, healthcare, government, or similar)\nFamiliarity with spatial or layout-aware document representations\nExperience building systems that support explainability, traceability, and audit requirements\nExposure to hybrid approaches that combine rules, heuristics, and learned models\nAbility to reason about tradeoffs between precision, recall, automation rate, and operational risk\nJob Type: Full-time\nPay: $130,000.00 - $170,000.00 per year\nBenefits:\n401(k)\n401(k) matching\nDental insurance\nHealth insurance\nLife insurance\nPaid time off\nProfessional development assistance\nVision insurance\nWork Location: Remote","datePosted":"2026-04-14T11:14:26.851Z","dateModified":"2026-04-14T11:14:26.851Z","hiringOrganization":{"@type":"Organization","name":"Iconect Development","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote","addressRegion":"OR","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"4784c3a1d89b516180d2ecb7"},"url":"https://jobsearcher.com/jobs/4784c3a1d89b516180d2ecb7"}}