Senior​/Principal Machine Learning Engineer

Senior/Principal Machine Learning Engineer

Be among the first 25 applicants.

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 have your back.

In return, we 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.

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.

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 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.

P5, Principal Machine Learning Engineer

• 10+ years experience building applied machine learning products at scale
• 4+ years professional experience with PyTorch, Tensor Flow, and other DL frameworks
• 6+ years building services to host ML models in production at scale
• 3+ years demonstrated experience with large language models (LLMs) and graph neural network models for real‑world use cases
• 6+ years proven experience with cloud platforms (AWS, GCP, etc.)
• Proven track record of leading, mentoring, and managing ML engineering teams, ownership of development lifecycle, sprint planning, and fostering collaboration
• Bachelor’s (Master’s or PhD preferred) in engineering, computer science, physics, math, or equivalent

P4, Senior Machine Learning Engineer

• 7+ years experience building applied machine learning products at scale
• 3+ years professional experience with PyTorch, Tensor Flow, and other DL frameworks
• 4+ years building services to host ML models in production at scale
• 2+ years demonstrated experience with large language models (LLMs) and graph neural network models for real‑world use cases
• 4+ years proven experience with cloud platforms (AWS, GCP, etc.)
• Proven track record of leading, mentoring, and managing ML engineering teams, ownership of…