Machine Learning Engineer - Document Intelligence

Workday • Pleasanton, CA

3d • Hybrid

About The Position

This is an exciting opening in the AI Platform team within the Document Intelligence group. The Document Intelligence team builds AI/ML-powered solutions to extract actionable insights from unstructured documents. We design scalable document processing pipelines that can ingest and interpret large volumes of data with minimal manual intervention. Our work includes advanced document parsing using NLP, computer vision, and large language models (LLMs), along with in-house model training for entity resolution. We integrate seamlessly with business workflows for areas like financials, spend management, and more. By continuously evolving our models to handle new document types and edge cases, we help automate and accelerate critical business processes across the organization. Workday’s AI Platform organization is bringing “AI first” products to life at every step of the Workday product offering. We’re looking for highly creative, results-focused, and deeply skilled Machine Learning Engineers/scientists to work with us on a range of these challenges.

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 Haves

• 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.
• Proficiency in large-scale data processing (PySpark, SQL).
• 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.

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.

Benefits

• Workday Bonus Plan or a role-specific commission/bonus
• Annual refresh stock grants
• Comprehensive benefits in Canada
• Comprehensive benefits in the US

What This Job Offers

• Job Type: Full-time
• Career Level: Senior