Sr Machine Learning Engineer

Workday • Vancouver, BC

31d • Hybrid

About The Position

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. The minute you join, you’ll 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. 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. Whether you're building smarter solutions, supporting customers, or creating a space where everyone belongs, you’ll do meaningful work with Workmates who’ve got your back. In return, we’ll 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, you’ve found a match in Workday, and we hope to be a match for you too. About the Team Do you want to build AI-powered software that impacts millions of people every day? The AI Core team, part of Workday’s AI Platform organization, tackles challenging problems at the intersection of machine learning, agentic reasoning, and enterprise-scale systems. Our work delivers critical AI platform capabilities and differentiated, deep-value agent applications. About the Role As a Senior Machine Learning Engineer on the AI Core team, you will be primarily responsible for designing, building, and applying machine learning models and agentic systems that power AI-driven applications at Workday. Specifically, you will:

Requirements

• 8+ years of professional Machine Learning / AI engineering experience, including designing, building, and scaling production ML models and systems.
• 5+ years of experience with advanced Python development.
• Bachelor’s degree in Computer Science, Machine Learning, or related discipline, or equivalent practical experience.
• Expertise in designing, training, and evaluating Machine Learning models (e.g., LLMs, deep learning models, classical ML) and deploying them to production environments.
• Expertise with building agentic systems and leveraging LLMs, retrieval-augmented generation (RAG), and sophisticated prompting techniques.
• Proficiency with popular Machine Learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn) and MLOps tools.
• Strong understanding of data wrangling, feature engineering, and data validation techniques for large-scale datasets.
• Proficiency with advanced Python concepts, such as asynchronous and concurrent programming, generators, higher-order abstractions, and applying object-oriented design principles.
• Proficiency with unix systems and cloud platforms, including containerized workloads and orchestration systems (e.g., AWS or GCP, Docker, Kubernetes).
• Ability to collaborate effectively across teams, working closely with other engineers while maintaining independent execution.
• Ownership mindset, able to take responsibility for a work area and deliver high-quality, reliable systems.
• Ability to mentor and coach other engineers, promoting best practices and raising the engineering bar.
• Architectural thinking skills, with the ability to contribute meaningful ideas and practical solutions in design and architecture discussions.
• Ability to communicate complex technical concepts clearly to both technical and non-technical stakeholders.

Responsibilities

• Lead the design, development, and deployment of novel agentic systems and core machine learning models that power AI-driven capabilities.
• Execute data analysis, error analysis, and rigorous experimentation to drive model improvements and new capability development.
• Design and implement the end-to-end machine learning pipeline (MLOps), ensuring model scalability, reliability, and consumption via robust APIs.
• Work with large-scale datasets to perform data wrangling, feature engineering, and validation to train and fine-tune state-of-the-art models.
• Apply machine learning and distributed systems principles in production to address model scalability, concurrency, fault tolerance, and performance challenges.
• Own ML models and systems through their full lifecycle, including deployment, monitoring, debugging, and ongoing operational improvements.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level