Senior Machine Learning Engineer/Machine Learning Engineer

Who you are

• P4, Senior Machine Learning Engineer:
• 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
• Bachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalent
• P3, Machine Learning Engineer III:
• 5+ 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
• 2+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow
• 3+ years of professional experience in building services to host machine learning models in production at scale
• 1+ years of demonstrated experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases
• 3+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.)
• Bachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalent

Other Qualifications:

• 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

What the job involves

• 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 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