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 Role
As a Senior/Principal Machine Learning Engineer in Agent Factory, you'll design and build the core ML systems behind Workday's next generation of AI agents. Working within a small, senior, cross-functional pod, you'll own how models, agent logic, and orchestration layers come together in production—across the full lifecycle from problem framing and data strategy to deployment, monitoring, and continuous improvement. You'll implement and evolve frameworks for LLM-powered agents, including RAG pipelines, workflow orchestration, evaluation, and feedback loops, ensuring solutions are scalable, observable, and enterprise-ready. This role sits at the intersection of ML and platform engineering: partnering closely with software engineers, product managers, and data scientists to integrate agents deeply into the Workday stack. You'll stay hands-on with emerging techniques in agentic architectures while applying strong engineering judgment to turn them into systems that are reliable, explainable, and built to operate at global scale.
About You
P5, Principal Machine Learning Engineer
Basic Qualifications:
10+ years experience as a member of a data science, machine learning engineering, or other relevant software development team building applied machine learning products at scale, including taking products through applied research, design, implementation, production, and production-based evaluation
4+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow
6+ years of professional experience in building services to host machine learning models in production at scale
3+ years of demonstrated experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases
6+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.)
Proven track record of successfully leading, mentoring, and/or managing ML Engineering teams, taking ownership of development lifecycle and sprint planning; fostering a culture of collaboration, transparency, innovation, and continuous improvement
Bachelor's (Master's or PhD preferred) degree in engineering, computer science, physics, math or equivalent
P4, Senior Machine Learning Engineer
Basic Qualifications:
7+ years experience as a member of a data science, machine learning engineering, or other relevant software development team building applied machine learning products at scale, including taking products through applied research, design, implementation, production, and production-based evaluation
3+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow
4+ years of professional experience in building services to host machine learning models in production at scale
2+ years of demonstrated experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases
4+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.)
Proven track record of successfully leading, mentoring, and/or managing ML Engineering teams, taking ownership of development lifecycle and sprint planning; fostering a culture of collaboration, transparency, innovation, and continuous improvement
Bachelor's (Master's or PhD preferred) degree in engineering, computer science, physics, math or equivalent
Other Qualifications:
Stay up to date with advancements in AI, LLMs, RAG, autonomous agents and orchestration frameworks to drive innovation
Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use cases
Professional experience in independently solving ambiguous, open-ended problems and technically leading teams
Excellent interpersonal and communication skills, with the ability to build strong relationships across teams and stakeholders
Proven track record of successfully leading, mentoring, and/or managing ML Engineering teams, taking ownership of development lifecycle and sprint planning; fostering a culture of collaboration, transparency, innovation, and continuous improvement.