Machine Learning Engineer III - Document Intelligence

Overview In this role, you will help design and build the core Document Intelligence Platform as a Service, shaping AI-powered document processing capabilities at scale. You’ll work with cross-functional teams to deliver entity extraction, entity resolution, and classification using cutting-edge ML/AI techniques. You’ll optimize models, build scalable pipelines, and perform rigorous data analysis to drive accurate, efficient outcomes. This is a high-impact opportunity to advance AI-first products that automate critical business workflows and enable the broader Workday customer base to operate more effectively. Compensation / Benefitsflexible work arrangementsbonus planstock grantscompetitive base payinclusive and accessible hiringopportunities for remote collaboration Responsibilities Design and implement core Document Intelligence Platform features (entity extraction, entity resolution, document classification)Develop and optimize LLM-based document parsing and classification solutions Build scalable ML pipelines for data preprocessing, feature engineering, training, and inference Perform exploratory data analysis on diverse document datasets to inform model improvements Collaborate with software engineers, app developers, product managers, and other ML teams Write clean, testable code and promote good software engineering practices (automation, observability, scalability)Participate in code reviews, design discussions, and team knowledge-sharing activities Key requirements 3+ years in research, development, and deployment of production-grade ML systems (DL, NLP, IR, recommender systems)Experience with PyTorch or Tensor Flow Proven track record in NLP/LLM products, including RAG architectures and long-context LLM applications 2+ years Python experience with modular design, asynchronous patterns, scalable architecture Advanced degree in a quantitative field or strong peer-reviewed publications Proficiency in techniques like RL, imitation learning, graph neural networks, multi-modal models Experience with production MLOps, model fine-tuning (PEFT), evaluation frameworks, and cloud deployments (Docker/K8s, AWS/GCP)Strong collaboration and ability to lead cross-functional workstreams Collaborative mindset Autonomy and initiative Problem-solving with creative solutions PyTorch Tensor FlowNLP