Machine Learning Engineer, Workday

• 3+ yrs experience as a member of a 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 with Python and supporting numeric libraries, with experience in shipping production code and models
• 2+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow, Huggingface
• 2+ years of professional experience in building services to host machine learning models in production at scale with cloud computing platforms (e.g. AWS, GCP, etc.)
• 2+ years of professional experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases
• Bachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalent

Desirable

• You’ll work at the intersection of AI, platform architecture, and human workflows, with the autonomy to shape how agents reason, act, and scale responsibly. High trust, high expectations, and real impact. Engineering, but brighter
• As a 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