The role focuses on building the critical infrastructure that empowers over 65% of the Fortune 500 through Agent Optimization and Information Retrieval
Job Summary
The role focuses on building the critical infrastructure that empowers over 65% of the Fortune 500 through Agent Optimization and Information Retrieval.
Engineers will architect sophisticated reasoning and planning agents while driving meta-ML optimization for automated node-level improvements.
Candidates must bridge the gap between deep research and production deployment to deliver transformative value to millions of global users.
Matching Summary
The role focuses on building the critical infrastructure that empowers over 65% of the Fortune 500 through Agent Optimization and Information Retrieval.
Salary
Base: $156,000 - $234,000 CAD (Toronto); Base: $163,000 - $288,000 USD (US locations); Bonus/Equity: Eligible for Workday Bonus Plan and stock grants
Skills & Requirements
Must-have
3+ years production-grade ML systems experience
Deep learning NLP Information Retrieval expertise
PyTorch or TensorFlow framework proficiency
RAG architectures and agentic frameworks knowledge
Text-to-SQL long-context LLM applications
Expert-level Python modular library design
Nice-to-have
DSPy Reinforcement Learning imitation learning
Graph neural networks multi-modal models
Knowledge Graphs Golden Dataset curation
Cross-functional team leadership and mentorship
Test-everything mindset with A/B testing
Sustainable work-life integration culture
Key Requirements
3+ years ML system development (MLE III) or 6+ years (Senior MLE)
Master's or Ph.D. in quantitative field preferred
Peer-reviewed research publications portfolio
Hands-on MLOps and cloud-native deployment experience