Machine Learning Engineer III / Senior Machine Learning Engineer - AI Platform

Your work days are brighter here. We’re obsessed with making hard work pay off, for our people, our customers, and the world around us. As a Fortune 500 company and a leading AI platform for managing people, money, and agents, we’re shaping the future of work so teams can reach their potential and focus on what matters most. The minute you join, you’ll feel it. Not just in the products we build, but in how we show up for each other. Our culture is rooted in integrity, empathy, and shared enthusiasm. We’re in this together, tackling big challenges with bold ideas and genuine care. We look for curious minds and courageous collaborators who bring sun-drenched optimism and drive. Whether you're building smarter solutions, supporting customers, or creating a space where everyone belongs, you’ll do meaningful work with Workmates who’ve got your back. In return, we’ll give you the trust to take risks, the tools to grow, the skills to develop and the support of a company invested in you for the long haul. So, if you want to inspire a brighter work day for everyone, including yourself, you’ve found a match in Workday, and we hope to be a match for you too. About the Team This is a very exciting opening in the AI Platform team in our Agent Optimization & Evaluation, and Information Retrieval team.. We are the Optimization and "Ground Truth" engine for Workday’s AI transformation, building the critical infrastructure that empowers over 65% of the Fortune 500. Our mission is two-fold: 1. Agent Optimization & Evaluation: Providing the algorithms and rigorous data-driven frameworks to validate, scale, and optimize AI agents across our entire enterprise suite. 2. Information Retrieval: Developing the intelligence layer that bridges human language and enterprise data through advanced semantic search and natural language-to-code (SQL/Python) execution. Why Join Us? 1. The Data & Frontier: Solve unique challenges in Agentic AI using exclusive, high-integrity enterprise datasets. 2. Impact at Scale: Your work acts as the optimizer and gatekeeper of quality for products reaching 31 million users globally. 3. People-First Culture: We balance high-intensity innovation with a commitment to sustainable work-life integration. We are looking for creative, results-focused ML Engineers and Senior ML Engineers to help us build the next generation of "AI-first" products. About the Role We are seeking pragmatic ML and Senior ML Engineers to drive the applied research, deployment, and optimization of our Agentic AI, Search, and Semantic Parsing products. In this role, you will bridge the gap between deep research and production, embedding cutting-edge agents directly into the Workday ecosystem. Leveraging our vast computing power and exclusive datasets, you will solve complex technical challenges to deliver transformative value to millions of users. If you are ready to apply creative problem-solving to global-scale ML systems, we want to hear from you. In this role, you would: Architect Agentic AI: Design and deploy sophisticated reasoning, planning, and swarm agents that interact seamlessly with enterprise data and support continuous, life-long learning. Drive Meta-ML & Optimization: Develop algorithms for automated node-level optimization within agent graphs, identifying the best LLM and prompt configurations for every workflow step. Build recommender systems for engineering teams to drive optimal evaluation for their agents. Advance Information Retrieval: Build hybrid, agentic search systems and semantic parsing products (Text-to-SQL/Python) utilizing vector search, reasoning, and fine-tuning for structured output. Scale Evaluation & Observability: Engineer cloud-based pipelines (Kubeflow) and A/B testing frameworks for rigorous offline/online evaluation, failure attribution, and safety monitoring. Lead the ML Lifecycle: Own the end-to-end MLOps process—from exploration and prompt engineering to scalable production deployment—ensuring high ... (truncated, view full listing at source)