Design and build the core ML systems behind Workday’s next generation of AI agents
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
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
Partner closely with software engineers, product managers, and data scientists to integrate agents deeply into the Workday stack
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

Requirements

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
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

Benefits

Workday Bonus Plan or role-specific commission/bonus
Annual refresh stock grants

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningdeep learningPytorchTensorFlowlarge language modelstext generation modelsgraph neural networkscloud computingRAG pipelinesworkflow orchestration

Soft Skills

leadershipmentoringcollaborationtransparencyinnovationcontinuous improvementengineering judgmentproblem framingdata strategysprint planning