Machine Learning Engineer III - Document Intelligence

Workday • Pleasanton, CA

1d • $136,200 - $240,000 • Hybrid

About The Position

The Machine Learning Engineer will join Workday’s AI Platform, within the ML - Document Intelligence team, to design and develop its core Document Intelligence Platform as a Service. The role focuses on features such as generic document entity extraction, entity resolution, and document classification using current AI and machine learning methods.

The engineer will support LLM-based document parsing, extraction, and classification; apply traditional machine learning and deep learning to improve model accuracy, efficiency, and scalability; and build scalable pipelines and services for preprocessing, feature engineering, training, and inference. The role also involves exploratory analysis of document datasets to inform feature engineering and model development.

The position collaborates with software engineers, Workday application developers, product managers, and other machine learning teams. The engineer is expected to take ownership of creative solutions, write clean and maintainable testable code, follow engineering practices for automation, observability, and scalability, conduct code reviews, participate in design discussions, and contribute to team activities such as hackathons and knowledge-sharing sessions.

Requirements

• At least 3 years of experience researching, developing, and deploying production-grade machine learning systems.
• Expertise in deep learning, NLP, information retrieval, and recommender systems using frameworks such as PyTorch or TensorFlow.
• A proven track record of building and evaluating NLP- and LLM-powered products, including expertise in RAG architectures, agentic frameworks such as LangChain or LangGraph, and long-context LLM applications such as Text-to-SQL.
• At least 2 years of Python experience focused on modular library design, asynchronous patterns, and scalable system architecture, including state management and error handling for non-deterministic AI outputs.
• Experience or proficiency in large-scale data processing with PySpark and SQL.

Nice To Haves

• An advanced degree (Master’s or Ph.D.) in a quantitative field or a strong portfolio of peer-reviewed research publications.
• Proficiency in DSPy, reinforcement learning, imitation learning, graph neural networks, multimodal models, and large-scale data processing.
• Experience with A/B testing, Knowledge Graphs, and curating Golden Datasets for model benchmarking.
• Hands-on experience across the ML lifecycle, including model fine-tuning with PEFT, evaluation frameworks such as DeepEval or RAGAS, and cloud-native deployment with Docker/Kubernetes and AWS or 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.
• Apply traditional machine learning and deep learning techniques to improve the accuracy, efficiency, and scalability of document intelligence models.
• Build scalable machine learning pipelines and services for data preprocessing, feature engineering, training, and inference for high-volume document processing.
• Perform exploratory data analysis on document datasets to identify insights, optimize feature engineering, and inform model development.
• Collaborate with software engineers, Workday application developers, product managers, and other machine learning teams.
• Take ownership of 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.

Benefits

• May be eligible for the Workday Bonus Plan or a role-specific commission/bonus.
• May be eligible for annual refresh stock grants.
• Workday comprehensive benefits are referenced; specific benefits are not listed in the posting.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed