Senior​/Principal Machine Learning Engineer

Senior/Principal Machine Learning Engineer

Workday, Inc.

Remote friendly (USA, CA, Pleasanton United States of America)

Workday is a Fortune 500 company and a leading AI platform for managing people, money, and agents. We’re obsessed with making hard work pay off, for our people, our customers, and the world around us. As we shape the future of work, you’ll feel what we build and how we show up for each other. Our culture is rooted in integrity, empathy, and shared enthusiasm.

We look for curious minds and courageous collaborators who bring sun‑drenched optimism and drive.

Agent Factory is where Workday’s next chapter gets built. We’re forming small, senior, cross‑functional AI teams that bring together product leaders, machine learning engineers, and full‑stack builders to create intelligent agents used by millions of people every day. This is production‑grade AI—deeply embedded into Workday’s platform—not research experiments or maintenance work. Teams own problems end to end, collaborate tightly across disciplines, and use the right tools to solve real customer challenges at global scale.

As a Senior/Principal Machine Learning Engineer, 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.

• 10+ years experience in a data science, machine learning engineering, or 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, Tensor Flow.
• 6+ years of professional experience 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.

• 7+ years experience in a data science, machine learning engineering, or 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, Tensor Flow.
• 4+ years of professional experience 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…