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.
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.
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.
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.
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.
About You P5, Principal Machine Learning Engineer Basic
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
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
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.
Experience
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
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
Benefits
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.
Workday Pay Transparency Statement The annualized base salary ranges for the primary location and any additional locations are listed below.
Workday pay ranges vary based on work location.
As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants.
Each candidate’s compensation offer will be based on multiple factors including, but not limited to, geography, experience, skills, job duties, and business need, among other things.
Primary Location: USA.CA.Pleasanton Primary Location Base Pay Range: $228,000 USD - $342,000 USD Additional US Location(s) Base Pay Range: $190,600 USD - $342,000 USD Our Approach to Flexible Work With Flex Work, we’re combining the best of both worlds: in-person time and remote.
Additional details
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.
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 Team Agent Factory is where Workday’s next chapter gets built.
Teams own problems end to end, collaborate tightly across disciplines, and use the right tools to solve real customer challenges at global scale.
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.