Machine operator • richmond bc
Architect & Build: Design, develop, and deploy scalable machine learning models and AI systems (ranging from predictive models to Generative AI and LLM-powered agents) that directly impact Workday'... Show more
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Assistant Manager, Superintendent or Assistant Superintendent.GENERAL DESCRIPTION / PURPOSE:.Machine Operator 1 (MO1), a field based position, is responsible for operating, maintaining, and repairi... Show more
As a 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... Show more
About the Role
What you will do:
Architect & Build: Design, develop, and deploy scalable machine learning models and AI systems (ranging from predictive models to Generative AI and LLM-powered agents) that directly impact Workday's core enterprise applications.
End-to-End Ownership: Take full ownership of the ML lifecycle, including data extraction, feature engineering, model training, deployment, optimization, and continuous monitoring in a high-scale production environment.
Cross-Functional Collaboration: Partner closely with Data Scientists, Software Engineers, Product Managers, and UX Designers to translate complex business problems into robust AI solutions.
Drive Technical Excellence: Establish and advocate for engineering best practices, robust MLOps processes, and highly optimized code.
Mentorship & Leadership: Guide and mentor junior engineers, conduct code and architecture reviews, and help shape the technical roadmap for your team.
Champion Responsible AI: Ensure all models adhere to Workday's strict standards for data privacy, security, fairness, and ethical AI practices.
About You
Basic Qualifications
Experience: 7+ years of industry experience in software engineering with a strong focus on applied machine learning, deep learning, or NLP.
Programming Mastery: Expert-level proficiency in Python and strong software engineering fundamentals (data structures, algorithms, object-oriented design).
ML Frameworks: Deep hands-on experience with industry-standard machine learning and deep learning libraries (e.g., PyTorch, TensorFlow, Scikit-learn, Hugging Face).
Production Deployment: Proven track record of taking ML models out of research/notebook environments and deploying them into scalable, high-traffic production systems.
Cloud & Infrastructure: Solid experience with cloud computing platforms (AWS or GCP) and modern infrastructure tools (Docker, Kubernetes).
Education: Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or a related highly quantitative field (or equivalent practical experience).
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. 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 CAN Base Pay Range: $156,000 - $234,000 CADAdditional CAN Location(s) Base Pay Range: $156,000 - $234,000 CAD
