Senior Machine Learning Engineer

Workday Workday · Enterprise · Toronto, ON +1

Workday is seeking a Senior Machine Learning Engineer to join their AI Team in Canada. The role involves architecting, building, and deploying scalable ML models and AI systems, including predictive models, Generative AI, and LLM-powered agents, impacting Workday's core enterprise applications. The engineer will own the end-to-end ML lifecycle, collaborate with cross-functional teams, drive technical excellence, mentor junior engineers, and champion Responsible AI. Requires 7+ years of experience in applied ML/DL/NLP, Python proficiency, experience with ML frameworks (PyTorch, TensorFlow), production deployment, and cloud platforms (AWS/GCP).

What you'd actually do

  1. 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.
  2. 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.
  3. Cross-Functional Collaboration: Partner closely with Data Scientists, Software Engineers, Product Managers, and UX Designers to translate complex business problems into robust AI solutions.
  4. Drive Technical Excellence: Establish and advocate for engineering best practices, robust MLOps processes, and highly optimized code.
  5. Mentorship & Leadership: Guide and mentor junior engineers, conduct code and architecture reviews, and help shape the technical roadmap for your team.

Skills

Required

  • Python
  • PyTorch
  • TensorFlow
  • Scikit-learn
  • Hugging Face
  • AWS
  • GCP
  • Docker
  • Kubernetes
  • software engineering fundamentals
  • data structures
  • algorithms
  • object-oriented design
  • applied machine learning
  • deep learning
  • NLP

Nice to have

  • Generative AI
  • LLM-powered agents
  • information retrieval
  • recommendation systems

What the JD emphasized

  • production-grade AI solutions
  • LLM-powered agents
  • highly precise information retrieval
  • recommendation systems
  • production deployment
  • scalable, high-traffic production systems

Other signals

  • building the intelligence layer that powers the future of work
  • embedding cutting-edge machine learning, Generative AI, and autonomous agents
  • build robust, production-grade AI solutions that solve real business challenges at global scale
  • develop sophisticated LLM-powered agents
  • engineering highly precise information retrieval and recommendation systems