Senior Machine Learning Engineer

Workday
Ireland, DublinPosted 3 April 2026

Job Description

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 It's fun to work in a company where people truly believe in what they're doing. At Workday, we're committed to bringing passion and customer focus to the business of enterprise applications. We work hard, and we're serious about what we do. But we like to have a good time, too. In fact, we run our company with that principle in mind every day: One of our core values is fun. Join the Deployment Adoption Platform team, where we are building an AI platform to reduce the cost of implementing Workday globally. About the Role Your technical contributions and team-first mindset will play a key role in delivering high-impact products that utilize AI to reduce the cost of implementing Workday globally. About You Basic Qualifications 8 years of experience in software engineering Expert level proficiency in Python Experience in LangGraph, LangChain, and LangSmith Proficiency in at least one other statically-typed language such as Java, Scala, Rust, or C Demonstrated, hands-on experience building applications with Large Language Models Other Qualifications Experience implementing agentic design patterns where an LLM uses tools to interact with its environment Experience developing and deploying applications on a major cloud platform (AWS, GCP, or Azure). Experience architecting complex, event-driven systems capable of handling asynchronous tasks. Proven success working within fast-paced, agile environments and cross-functional teams Strong communication skills, with the ability to collaborate with both technical and non-technical partners Comfortable working with ambiguity and translating complex problems into clear, thoughtful solutions Committed to fostering an inclusive, team-oriented environment and contributing to a culture of continuous improvement Good understanding of Software Development principles and experience in deploying and scaling machine learning models in production environments. Strong Team Collaboration skills and the ability to mentor and guide other team members. Our Approach to Flexible Work With 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 importa ... (truncated, view full listing at source)
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