Machine Learning Engineer III - Document Intelligence

We are looking for a Machine Learning Engineer to join the ML - Document Intelligence team to drive the design and development of our core Document Intelligence Platform as a Service. In this role, you will work on building and optimizing critical features like generic document entity extraction, entity resolution, and document classification, leveraging 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 our document intelligence models; Build scalable ML pipelines and services for data preprocessing, feature engineering, training, and inference, enabling high-volume document processing workflows; Perform exploratory data analysis (EDA) on diverse document datasets to uncover valuable insights, optimize feature engineering, and inform model development; Collaborate with software engineers, Workday app developers, product managers, and other ML teams; Take ownership for finding creative solutions that move projects forward; Write clean, maintainable, and testable code following best practices in software engineering, including automation, observability, and scalability; Conduct code reviews, participate in design discussions, and engage in collaborative team activities like hackathons and knowledge-sharing sessions

Requirements

3+ years of experience researching, developing and deploying production-grade ML systems, including expertise in deep learning, NLP, Information Retrieval, and recommender systems using frameworks like PyTorch or TensorFlow; Proven track record of building and evaluating NLP and LLM-powered products, including expertise in RAG architectures, agentic frameworks (e.g., LangChain/LangGraph), and long-context LLM applications (e.g., Text-to-SQL); 2+ years of Python experience with a focus on modular library design, asynchronous patterns, and scalable system architecture (state management/error handling) for non-deterministic AI outputs

Nice to have

Advanced degree (Master’s or Ph.D.) in a quantitative field or a strong portfolio of peer-reviewed research publications; Proficiency in techniques like DSPy, Reinforcement Learning, imitation learning, graph neural networks, multi-modal models, and large-scale data processing (PySpark, SQL); A 'test-everything' mindset with experience in A/B testing, Knowledge Graphs, and 'Golden Dataset' curation for model benchmarking; Hands-on experience with the full ML lifecycle, including model fine-tuning (PEFT), evaluation frameworks (e.g., DeepEval/RAGAS), and cloud-native deployment (Docker/K8s, AWS/GCP); Demonstrated ability to lead cross-functional teams, mentor junior engineers, and solve ambiguous problems with high autonomy

Benefits

Eligibility for the Workday Bonus Plan or role-specific commission/bonus; Annual refresh stock grants; Flexible work schedule