Machine Learning Engineer – Document Intelligence
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Job Description
About Workday:
Workday is a company that develops enterprise software. Its culture is employee-centric and collaborative, prioritizing the happiness, development, and contribution of its employees (“Workmates”). Workday emphasizes a balance between profitability and looking after its people, communities, and the planet. The company encourages employees to be themselves and fosters a passionate and energetic work environment. Workday emphasizes the importance of candidate privacy and data security and will never ask candidates to pay fees or use non-Workday career sites for applications.
About the Job: Machine Learning Engineer – Document Intelligence
Workday’s Document Intelligence team creates AI/ML-powered solutions for extracting insights from unstructured documents. They build scalable document processing pipelines handling large data volumes with minimal manual intervention. This involves advanced document parsing using NLP, computer vision, and LLMs, along with in-house model training for entity resolution. The team integrates their solutions with business workflows in areas like financials and spend management. They continually improve models to handle new document types and edge cases, automating and accelerating critical business processes.
Job Description:
The Machine Learning Engineer will contribute to the design and development of the core Document Intelligence Platform as a Service. Key responsibilities include:
• LLM-based Technology Development: Supporting the design and implementation of LLM-based technologies for document parsing, entity extraction, and classification.
• Model Enhancement: Applying traditional ML and deep learning techniques to improve the accuracy, efficiency, and scalability of document intelligence models.
• Scalable ML Pipeline Development: Building scalable ML pipelines and services for data preprocessing, feature engineering, training, and inference, enabling high-volume document processing.
• Exploratory Data Analysis (EDA): Performing EDA on diverse document datasets to uncover insights, optimize feature engineering, and inform model development.
Additional Responsibilities:
• Collaboration with software engineers, Workday app developers, product managers, and other ML teams.
• Taking ownership of finding creative solutions to advance projects.
• Writing clean, maintainable, and testable code using best software engineering practices (automation, observability, scalability).
• Conducting code reviews, participating in design discussions, and engaging in team activities (hackathons, knowledge sharing).
Qualifications:
• Basic Qualifications:
• 4+ years’ experience in a data science, machine learning software development team, or relevant PhD program experience.
• 3+ years’ experience with Python and ML frameworks like PyTorch or TensorFlow.
• 2+ years’ experience in machine learning, deep learning, NLP, GenAI, multi-agent AI systems, distributed training, model hosting, etc.
• 3+ years’ experience handling large-scale, complex datasets, data modeling, and productizing machine learning algorithms.
• Other Qualifications:
• Strong knowledge of classical machine learning and deep learning.
• Working knowledge of LLMs and their use in building agentic systems.
• Proficiency with Spark for large-scale data processing.
• Excellent communication and collaboration skills.
