Machine Learning Engineer

**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. 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 AI Platform Information Retrieval team is at the heart of Workday’s intelligence layer. We don’t just find documents; we bridge the gap between human language, search, and enterprise data, including reasoning over knowledge. Our products utilize advanced semantic search to navigate Workday’s massive data model, turning natural language questions into precise SQL and Python executions. You’ll work in a high-growth environment where LLMs meet structured enterprise systems, building the agents that make "Natural Language as a UI" a reality at scale.

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.

The Data: Work with exclusive, high-integrity enterprise datasets that most researchers never see.

The Scale: Your code will empower the world’s largest companies to make data-driven decisions.

The Culture: A "people-first" environment that balances high-intensity innovation with sustainable work-life integration.**About the Role****In this role, you would:**Advanced Information Retrieval: Build and refine hybrid search systems (Vector, Keyword, Agentic, and Multi-step Reasoning) that navigate Workday’s proprietary data objects with high precision and recall. Apply deep learning techniques to enhance recommender systems, ranking models, and information retrieval systems, driving relevance and personalization across core Workday applications.Semantic Parsing & Code Gen: Design and optimize LLM, model and agent based products for Text-to-SQL and Text-to-Python, focusing on schema grounding, few-shot prompting, and fine-tuning for structured output.Evaluation Platform Development: Establish rigorous and scalable methods for evaluating Information Retrieval products, AI agents, LLMs, and other ML model accuracy and performance, including developing metrics for quality, safety, latency, and user experience.AI Agent Engineering: Design, build, and deploy sophisticated AI agents (e.g. orchestration agents, planning and execution nodes, tool selection agents, autonomous workflow agents, conversational interface agents) that interact seamlessly with enterprise data. Work on continuous learning for the agents.Prompt Engineering & Optimization: Develop, test, and maintain advanced prompt engineering and prompt optimization strategies and guardrails to ensure LLM-powered features are accurate, safe, and reliable at scale.Production and MLOps: Own the entire ML lifecycle, ensuring high-quality, scalable deployment, monitoring, and continuous improvement of all models and agents in production environments.Own exploration, design and execution of advanced ML models, algorithms and frameworks that deliver value to our users.Collaborate with other ML engineers, software engineers, product managers, and across teams to deliver your products through Workday end user applications.Be given autonomy and ownership over your work, but with the support of the entire organization.Keep abreast of the latest advancements in ML/AI, Information Retrieval, Agentic AI, Generative AI, NLP research, techniques and tools.Have extraordinary opportunities for career growth and learning in a fast-growing, forward-looking company.**About You****Basic Qualifications**3+ years of professional experience as a Machine Learning Engineer, focusing on researching, developing, building, training, and deploying deep learning, Information Retrieval, NLP solutions, and generative/agentic AI systems into production.Proven, hands-on experience building and launching Generative AI products powered by long context LLMs, specifically applied to structured data tasks e.g. Text to SQL.Expertise in prompt engineering, prompt optimization, and developing robust strategies for integrating LLMs into user-facing products.Experience researching, developing and deploying production-grade recommender systems, information retrieval systems and/or ranking models, e.g. vector databases, embedding models, and RAG (Retrieval-Augmented Generation) architectures.Demonstrated experience in building and evaluating AI agents, including familiarity with agent frameworks, RAG architectures, and agent evaluation frameworks.Expertise in language model fine-tuning techniques (e.g., parameter-efficient fine-tuning, domain adaptation) and building models with mid and large model architectures (e.g., BERT family, as well as LLMs).Proven theoretical and practical understanding of statistical analysis and machine learning algorithms, natural language processing, especially for supervised, unsupervised and self-supervised methods.Engineering Excellence: Expert-level Python skills, including topics relating to multi-threading, api design, matrix processing, runtime memory design, and asynchronous call patterns. Expected to have experience with modern ML frameworks (PyTorch, HuggingFace) and orchestration libraries (LangGraph, Haystack, or LlamaIndex or equivalent).Systems Design: Strong understanding of how to build scalable "Agent-in-the-loop" systems, including error handling and state management.Take ownership for finding creative algorithmic and system design solutions that move projects, work-streams and products forward. Have determination to turn ideas into reality and improve user experience.Solid understanding and experience with MLOps, scalability, and cloud services (e.g., AWS, GCP, Azure), containerization technologies (e.g. Docker), Kubernetes, and large-scale ML systems.**Other Qualifications**A relevant advanced degree (Master’s or Ph.D.) in Computer Science, Machine Learning, AI, Computer/Software engineering or a related quantitative programming field.Experience with embeddings (text, multi-modal, and graph), vector databases, search indices, informational retrieval techniques, and large-scale data ingestion pipelines.Data Mastery: Proficiency in SQL and experience with large-scale data processing (Spark, Pandas).Schema Expertise: Familiarity with Knowledge Graphs or complex relational data modeling is a huge plus.Experience with advanced techniques such as reinforcement learning, imitation learning, graph neural networks, #J-18808-Ljbffr