Senior/Principal Machine Learning Engineer

Workday•Vancouver, BC

2d•Hybrid

About The Position

Workday is seeking a Senior/Principal Machine Learning Engineer to join their Agent Factory team. This role involves designing and building core ML systems for Workday's next generation of AI agents. You will work within a small, senior, cross-functional pod, owning the integration of models, agent logic, and orchestration layers in production. This includes the full lifecycle from problem framing and data strategy to deployment, monitoring, and continuous improvement. You will 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. The role is at the intersection of ML and platform engineering, requiring close partnership with software engineers, product managers, and data scientists to integrate agents deeply into the Workday stack. You will stay hands-on with emerging techniques in agentic architectures while applying strong engineering judgment to turn them into reliable, explainable systems built to operate at global scale. 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.

Requirements

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 (for Principal)
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 (for Senior)
4+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow (for Principal)
3+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow (for Senior)
6+ years of professional experience in building services to host machine learning models in production at scale (for Principal)
4+ years of professional experience in building services to host machine learning models in production at scale (for Senior)
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 (for Principal)
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 (for Senior)
6+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.) (for Principal)
4+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.) (for Senior)
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.
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.

Nice To Haves

Stay up to date with advancements in AI, LLMs, RAG, autonomous agents and orchestration frameworks to drive innovation.

Responsibilities

Design and build the core ML systems behind Workday’s next generation of AI agents.
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.
Implement and evolve frameworks for LLM-powered agents, including RAG pipelines, workflow orchestration, evaluation, and feedback loops.
Ensure solutions are scalable, observable, and enterprise-ready.
Partner closely with software engineers, product managers, and data scientists to integrate agents deeply into the Workday stack.
Stay hands-on with emerging techniques in agentic architectures.
Apply strong engineering judgment to turn emerging techniques into systems that are reliable, explainable, and built to operate at global scale.
Lead, mentor, and/or manage ML Engineering teams, taking ownership of development lifecycle and sprint planning.
Foster a culture of collaboration, transparency, innovation, and continuous improvement.