Machine Learning Engineer Iii / Senior Machine Learning Engineer - Ai Platform

Workday

Toronto, Ontario, Canada
Base: $156,000 cad - $234,000 cad (toronto); base:...
Hybrid
3+ years production ml system experience
Deep learning nlp information retrieval expertise
Pytorch or tensorflow framework proficiency
This role serves as the Optimization and Ground Truth engine for Workday's AI transformation, empowering over 65% of the Fortune 500

Job Summary

  • This role serves as the Optimization and Ground Truth engine for Workday's AI transformation, empowering over 65% of the Fortune 500.
  • You will architect sophisticated reasoning and planning agents while driving meta-ML optimization to identify the best LLM configurations for every workflow step.
  • The position offers the opportunity to solve unique challenges in Agentic AI using exclusive, high-integrity enterprise datasets with a team committed to sustainable innovation.

Matching Summary

This role serves as the Optimization and Ground Truth engine for Workday's AI transformation, empowering over 65% of the Fortune 500.

Salary

Base: $156,000 CAD - $234,000 CAD (Toronto); Base: $163,000 USD - $288,000 USD (US locations); Bonus/Equity: Eligible for Workday Bonus Plan and annual refresh stock grants

Skills & Requirements

Must-have

  • 3+ years production ML system experience
  • Deep learning NLP Information Retrieval expertise
  • PyTorch or TensorFlow framework proficiency
  • RAG architectures and agentic frameworks
  • LangChain or LangGraph implementation skills
  • Text-to-SQL and long-context LLM applications
  • Expert-level Python asynchronous patterns

Nice-to-have

  • DSPy optimization techniques
  • Reinforcement learning and imitation learning
  • Graph neural networks and multi-modal models
  • A/B testing and golden dataset curation
  • Cross-functional leadership and mentorship
  • Sustainable work-life integration culture

Key Requirements

  • 3+ years experience for MLE III level
  • 6+ years experience for Senior MLE level
  • Master's or Ph.D. in quantitative field preferred
  • Peer-reviewed research publications portfolio
  • Hands-on MLOps and cloud-native deployment experience

Work Rights

Not specified

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