Senior/Principal Machine Learning Engineer

Your work days are brighter here.

Were 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, were shaping the future of work so teams can reach their potential and focus on what matters most. The minute you join, youll 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. Were 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 youre building smarter solutions, supporting customers, or creating a space where everyone belongs, youll do meaningful work with Workmates whove got your back. In return, well 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. So, if you want to inspire a brighter work day for everyone, including yourself, youve found a match in Workday, and we hope to be a match for you too.

About the Team

About the Role

As a Senior/Principal Machine Learning Engineer in Agent Factory, youll design and build the core ML systems behind Workdays next generation of AI agents. Working within a small, senior, cross-functional pod, youll 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. Youll 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. Youll 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.

About You

P5, Principal Machine Learning Engineer

Basic 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 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
• Bachelors (Masters or PhD preferred) degree in engineering, computer science, physics, math or equivalent

P4, Senior Machine Learning Engineer

Basic 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 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 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
• Bachelors (Masters 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.

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. Recruiters can share more detail during the hiring process. Each candidates compensation offer will be based on multiple factors including, but not limited to, geography, experience, skills, job duties, and business need, among other things. For more information regarding Workdays comprehensive benefits, please click here.

Our Approach to Flexible Work

With Flex Work, were combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role). This means youll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together. Those in our remote "home office" roles also have the opportunity to come together in our offices for important moments that matter.

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Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans.

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