Staff Machine Learning Software Engineer
On-siteToronto, Ontario, Canada
Job Summary
Develop and oversee implementation of strategic AI roadmaps aligned with organizational objectives, directing cross-functional collaboration to drive impactful outcomes and influence enterprise strategies. Lead multiple AI-focused initiatives of significant complexity, managing teams in AI Engineering while providing strategic direction and leadership to ensure successful execution of high-impact projects. Make independent decisions on program, technical, or operational strategy for the department, fostering strong internal and external relationships to solve highly complex problems broadly related to AI Engineering. This role supports RBC's AI Group, which accelerates the shift from early-stage projects to scaled client outcomes across generative and agentic AI use cases.
Required Qualifications
- Bachelor's degree in Computer Science, Statistics, Math, Engineering or a related technical field
- 10+ years of experience leading high-impact machine learning solutions in a product-centric environment
- Experience managing/mentoring high-performing teams of data scientists and engineers
- Proven expertise across the research and development lifecycle, from prototyping to production, with strong ability to engage stakeholders
- Deep expertise in machine learning, statistics, and data science with experience in optimization, A/B testing, and causal inference
- Possess a deep expertise in production ML infrastructure including model serving platforms, feature stores, and data pipelines, with ability to support teams in deploying and maintaining ML models at scale
- Exceptional communication skills and ability to translate complex concepts to diverse audiences
- Demonstrated a mastery of end-to-end ML system ownership in production environments, including model design, feature engineering, model architecture, drift detection, and serving infrastructure at scale
- A deep expertise in building systems that serve as shared ML platforms for multiple downstream teams and use cases
- A proven ability to lead cross-functional technical initiatives by coordinating with diverse stakeholder groups (feature providers, policy teams, client teams) while maintaining system integrity and translating technical results to non-technical audiences
- Big Data Analytics
- Machine Learning (ML)
- Software Engineering
- Software Product Design
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