About the TeamThis is a very exciting opening in the AI Platform team in our Document Intelligence team. We believe if you do what you love, you'll love what you do. There's a lot to love at Workday. We are part of a global, high-growth technology company and our team has the opportunity to develop the next generation of Workday's groundbreaking collaborative products supporting a customer base of more than 31 million strong. Over 65% of the Fortune 500 are Workday customers.
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.
About the RoleWe 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.
Your primary focus will be to:
- 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.
You will also:
- 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.
About YouBasic Qualifications:
- Deep Technical ML Capability: 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.
- Generative AI & Agentic Systems: 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).
- Engineering Excellence: 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.
Other Qualifications
- Academic Foundation: Advanced degree (Master's or Ph.D.) in a quantitative field or a strong portfolio of peer-reviewed research publications.
- Optimization & Advanced Techniques: Proficiency in techniques like DSPy, Reinforcement Learning, imitation learning, graph neural networks, multi-modal models, and large-scale data processing (PySpark, SQL).
- Experimental Rigor: A "test-everything" mindset with experience in A/B testing, Knowledge Graphs, and "Golden Dataset" curation for model benchmarking.
- Data Pipelines: Proficiency in large-scale data processing (PySpark, SQL).
- Production MLOps: 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).
- Collaborative Leadership: Demonstrated ability to lead cross-functional teams, mentor junior engineers, and solve ambiguous problems with high autonomy.
Workday Pay Transparency StatementThe annualized base salary ranges for the primary location and any additional locations are listed below. Workday pay ranges vary based on work location. As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants. Recruiters can share more detail during the hiring process. Each candidate's compensation offer will be based on multiple factors including, but not limited to, geography, experience, skills, job duties, and business need, among other things. For more information regarding Workday's comprehensive benefits in Canada, please click here. For more information regarding Workday's comprehensive benefits in the US, please click here.
Primary Location: USA.CA.Pleasanton
Primary Location Base Pay Range: $160,000 USD - $240,000 USD
Additional CAN Location(s) Base Pay Range: $128,000 - $192,000 CAD
Our Approach to Flexible WorkWith Flex Work, we're combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply
spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role). This means you'll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together. Those in our remote "home office" roles also have the opportunity to come together in our offices for important moments that matter.
