Senior/Principal Machine Learning Engineer

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. 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.About YouPrincipal Machine Learning Engineer - Basic Qualifications10+ 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 evaluation4+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow6+ 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 cases6+ 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 improvementBachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalentSenior Machine Learning Engineer - Basic Qualifications7+ 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 evaluation3+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow4+ 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 cases4+ 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 improvementBachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalentOther Qualifications:Stay up to date with advancements in AI, LLMs, RAG, autonomous agents and orchestration frameworks to drive innovationDeep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use casesProfessional experience in independently solving ambiguous, open-ended problems and technically leading teamsExcellent interpersonal and communication skills, with the ability to build strong relationships across teams and stakeholdersProven 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 StatementWorkday 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 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. For more information regarding Workday’s comprehensive benefits, please .Primary Location: CAN.ON.TorontoPrimary Location Base Pay Range: $180,000 CAD - $270,000 CADPrimary CAN Base Pay Range: $180,000 - $270,000 CAD