Machine Learning Engineer - Document Intelligence

About the Team Document Intelligence is part of the AI Platform organization, building AI/ML-powered solutions that extract actionable insights from unstructured documents. The team designs scalable document processing pipelines for large volumes of data, using advanced NLP, computer vision, and large language models. It supports workflows in finance, spend management, and other critical business processes. About the Role We are looking for a Machine Learning Engineer to join the AI – Document Intelligence team. The engineer will design and develop core services for the Document Intelligence Platform as a Service, building and optimizing features such as generic document entity extraction, entity resolution, and document classification using cutting‑edge AI/ML techniques. Responsibilities Support the design and implementation of LLM‑based technologies for document parsing, entity extraction, and classification tasks. Apply traditional ML and deep learning techniques to continuously enhance the accuracy, efficiency, and scalability of document intelligence models. Build scalable ML pipelines and services for data preprocessing, feature engineering, training, and inference to enable high‑volume document processing workflows. Perform exploratory data analysis on diverse document datasets to uncover insights, optimize feature engineering, and inform model development. Collaborate with software engineers, app developers, product managers, and other ML teams. Take ownership of finding creative solutions that move projects forward. Write clean, maintainable, and testable code following software engineering best practices, including automation, observability, and scalability. Conduct code reviews, participate in design discussions, and engage in team activities such as hackathons and knowledge‑sharing sessions. Basic Qualifications Deep technical ML capability with 3+ years of research, development, and deployment of production‑grade ML systems, including expertise in deep learning, NLP, information retrieval, and recommender systems using PyTorch or TensorFlow. Generative AI experience, including building and evaluating NLP and LLM‑powered products, RAG architectures, agentic frameworks, and long‑context LLM applications. Python programming (2+ years) focused on modular library design, asynchronous patterns, and scalable system architecture for non‑deterministic AI outputs. Other Qualifications Advanced degree (Master’s or Ph.D.) in a quantitative field or strong portfolio of peer‑reviewed research publications. Proficiency in techniques such as DSPy, reinforcement learning, imitation learning, graph neural networks, multimodal models, and large‑scale data processing (PySpark, SQL). Experimental rigor with A/B testing, knowledge graphs, and golden‑dataset curation for model benchmarking. Large‑scale data processing expertise (PySpark, SQL). Hands‑on experience with full ML lifecycle, including model fine‑tuning (PEFT), evaluation frameworks, and cloud‑native deployment (Docker, Kubernetes, AWS, GCP). Collaborative leadership, mentoring junior engineers, and solving ambiguous problems with high autonomy. Pay Transparency Statement Primary Location: USA – CA – Pleasanton – Base Pay Range: $160,000 USD – $240,000 USD. Additional Canada location(s) – Base Pay Range: $128,000 CAD – $192,000 CAD. Pay is based on geography, experience, skills, job duties, and business need. Compensation may include a bonus plan or commission, and annual stock grants. Our Approach to Flexible Work We encourage flexibility by requiring at least 50 % of time in the office or field each quarter. Remote roles may attend in‑office events when needed. Flexibility is designed to align with personal and team needs. Legal and EEO Statements Workday considers qualified applicants under Fair Chance law, including those with arrest and conviction records. Workday is an Equal Opportunity Employer, including individuals with disabilities and protected veterans. Workday provides an accessible and inclusive hiring experience; please email View email address on click.appcast.io if you require assistance or an accommodation at any point. #J-18808-Ljbffr