Senior Applied Machine Learning Engineer

Location: Remote (Continental US) Department: Product & Engineering Clearance: U.S. Citizen, Public trust eligible Nice to have: VA Cleared Role Overview We 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. You 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. Key Responsibilities Analyze large document collection to detect trends, anomalies, and emergent patterns that inform automation strategy and compliance workflows. Design and implement machine learning solutions that analyze scanned and born-digital documents to support automated privacy and compliance decisions Leverage existing structured signals (such as spatial coordinates, annotations, and metadata) to discover repeatable document patterns and reusable automation opportunities Develop classification and detection models that improve document understanding, including content type recognition and metadata validation Apply computer vision techniques to locate visually significant elements within documents, such as signatures, forms, images, or sensitive regions Build and tune text-based models to identify and interpret sensitive information across diverse document formats and quality levels Create decisioning logic that distinguishes between documents requiring review and those eligible for automated pass-through Integrate models and services into production-grade pipelines with attention to performance, scale, auditability, and failure handling Collaborate closely with platform, data, and domain experts to ensure solutions align with operational realities and regulatory expectations Monitor, evaluate, and continuously improve model performance using real-world feedback and downstream outcomes Required Skills & Experience Strong background in applied machine learning with real-world production deployments Hands-on experience with document analysis, document classification, or document understanding systems Proficiency in computer vision techniques for layout analysis, object detection, and region identification Solid grounding in text processing and natural language techniques for entity detection, pattern recognition, and content classification Experience working with noisy or imperfect inputs such as scanned documents, OCR output, or legacy data Ability to translate ambiguous business or compliance requirements into measurable technical outcomes Strong software engineering skills, including versioned deployments, testing, and maintainability of ML systems Comfort working with large datasets and iterating on models based on empirical results rather than theoretical perfection Preferred Qualifications Experience in regulated or compliance-driven domains (privacy, legal, healthcare, government, or similar) Familiarity with spatial or layout-aware document representations Experience building systems that support explainability, traceability, and audit requirements Exposure to hybrid approaches that combine rules, heuristics, and learned models Ability to reason about tradeoffs between precision, recall, automation rate, and operational risk Job Type: Full-time Pay: $130,000.00 - $170,000.00 per year Benefits: 401(k) 401(k) matching Dental insurance Health insurance Life insurance Paid time off Professional development assistance Vision insurance Work Location: Remote