Machine Learning Engineer in Vancouver

Your work days are brighter here. 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. 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.

About The Team

Do you want to be part of the team which is reinventing Enterprise Financials for the agentic age? The Financials Hyper Automation AI team is a small, advanced team of ML engineers charged with re-envisioning and building the next generation of ERP Financial and Accounting systems. We have an entrepreneurial mindset with a focus on creating delightful, intelligent experiences, leveraging the state-of-the-art in AI as well as strong, proven techniques from conventional machine learning. Working closely with product managers, we build advanced prototypes as well as deploy full production-grade features to solve our customer’s most pressing business challenges. Our current areas of development include strong reasoning agents for complex financial workflows and agentic self-improvement.

About The Role

As a ML Engineer on the FIN Hyperautomation AI team, you will develop intelligent user experiences powered by advanced tools like generative AI. You will work with other engineers to deliver ML solutions across Workday’s ML infrastructure, from training ML models to developing APIs, as well as managing the lifecycle of such products and services. You will be backed by Workday’s vast computing resources and rich datasets, to deliver transformative value to our customers.

Your specific responsibilities include:

• Own exploration, design, implementation, and deployment of features.
• The evaluation, scalability, and observability of these features.
• Apply ML techniques, including LLMs and natural language understanding, to intelligently process business documents and automate critical customer workflows.
• Stay up to date with advancements in AI, LLMs, RAG, autonomous agents and orchestration frameworks to drive innovation.
• Serve as a technical role model for more junior engineers.

About You

Basic Qualifications

• 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.
• 5+ years of professional experience with Python and supporting numeric libraries.
• 5+ years of professional experience with cloud computing platforms (e.g. AWS, GCP, etc.).
• Bachelor’s (Master’s or PhD preferred) degree in engineering, data/computer science, physics, math or equivalent.

Other Qualifications

• 5+ years of professional experience in building information retrieval systems.
• 5+ years of professional experience building scalable, production-level ML services on platforms such as Kubernetes.
• 5+ years of professional experience in ML and deep learning frameworks & toolkits, such as scikit-learn, Pandas, PySpark, Pytorch, TensorFlow.
• 3+ years of professional experience in developing with LLMs, RAG, and AI agents.
• Strong understanding of statistical analysis, ML paradigms and algorithms, and natural language processing.
• Professional experience in independently solving ambiguous, open-ended problems and technically leading a team.

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. This role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants.

Primary Location: CAN.BC.Vancouver

Primary CAN Base Pay Range: $128,000 - $192,000 CAD

Additional CAN Location(s) Base Pay Range: $128,000 - $192,000 CAD

Our Approach to Flexible Work

With Flex Work, we’re 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.