Senior 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
At Workday AI Team, we are building the intelligence layer that powers the future of work for millions of global users. Our AI organization is responsible for seamlessly embedding cutting-edge machine learning, Generative AI, and autonomous agents directly into Workday's core platform-optimizing the HR and financial operations of some of the world's largest enterprises. We don't just run sandbox experiments;
we build robust, production-grade AI solutions that solve real business challenges at global scale. As part of our team, you will operate at the intersection of deep applied research and scalable engineering. Whether we are developing sophisticated LLM-powered agents, advancing our next-generation AI engine (Workday Illuminate), or engineering highly precise information retrieval and recommendation systems, we leverage Workday's massive, clean, and exclusive datasets to deliver features that accelerate human workflows. We are a highly collaborative, cross-functional group of product leaders, data scientists, and ML engineers committed to the principles of Responsible AI. If you are a curious, courageous builder who wants to transition emerging AI capabilities into high-impact enterprise realities, you'll find a home with us.About the Role
What you will do:
Architect & Build: Design, develop, and deploy scalable machine learning models and AI systems (ranging from predictive models to Generative AI and LLM-powered agents) that directly impact Workday's core enterprise applications.End-to-End Ownership: Take full ownership of the ML lifecycle, including data extraction, feature engineering, model training, deployment, optimization, and continuous monitoring in a high-scale production environment.Cross-Functional Collaboration: Partner closely with Data Scientists, Software Engineers, Product Managers, and UX Designers to translate complex business problems into robust AI solutions.Drive Technical Excellence: Establish and advocate for engineering best practices, robust MLOps processes, and highly optimized code.Mentorship & Leadership: Guide and mentor junior engineers, conduct code and architecture reviews, and help shape the technical roadmap for your team.Champion Responsible AI: Ensure all models adhere to Workday's strict standards for data privacy, security, fairness, and ethical AI practices.About You
Basic Qualifications (Sr Machine Learning Engineer)
Experience: 7+ years of industry experience in software engineering with a strong focus on applied machine learning, deep learning, or NLP.Programming Mastery: Expert-level proficiency in Python and strong software engineering fundamentals (data structures, algorithms, object-oriented design).ML Frameworks: Deep hands-on experience with industry-standard machine learning and deep learning libraries (e.G., PyTorch, TensorFlow, Scikit-learn, Hugging Face).Production Deployment: Proven track record of taking ML models out of research/notebook environments and deploying them into scalable, high-traffic production systems.Cloud & Infrastructure: Solid experience with cloud computing platforms (AWS or GCP) and modern infrastructure tools (Docker, Kubernetes).Education: Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or a related highly quantitative field (or equivalent practical experience).Basic Qualifications (Machine Learning Engineer)
Experience: 5+ years of industry experience in software engineering with a strong focus on applied machine learning, deep learning, or NLP.Programming Mastery: Expert-level proficiency in Python and strong software engineering fundamentals (data structures, algorithms, object-oriented design).Luss
#J-18808-Ljbffr