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Restaurant Brands International Inc. is one of the world's largest quick service restaurant companies with nearly $45 billion in annual system-wide sales and over 32,000 restaurants in more than 120 countries and territories.

RBI owns four of the world's most prominent and iconic quick service restaurant brands – TIM HORTONS®, BURGER KING®, POPEYES®, and FIREHOUSE SUBS®. These independently operated brands have been serving their respective guests, franchisees and communities for decades. Through its Restaurant Brands for Good framework, RBI is improving sustainable outcomes related to its food, the planet, and people and communities.

RBI is committed to growing the TIM HORTONS®, BURGER KING®, POPEYES® and FIREHOUSE SUBS® brands by leveraging their respective core values, employee and franchisee relationships, and long track records of community support. Each brand benefits from the global scale and shared best practices that come from ownership by Restaurant Brands International Inc.

The AI / ML Data Engineer at RBI is responsible for building and operationalizing the data and machine learning infrastructure that powers enterprise forecasting and AI-driven insights. This role owns the pipelines, feature layers, and evaluation frameworks required to support scalable forecasting across sales, labor, inventory, and other operational domains, ensuring predictive outputs are accurate, governed, and grounded in source-of-truth enterprise data.

In addition, the role supports the development of RBI’s semantic and AI layers by modeling core datasets and enabling safe, performant natural language access to both descriptive and predictive insights. The AI / ML Data Engineer partners closely with AI Engineering, Data Science, Finance, Operations, and Technology teams to deliver trusted, production-grade ML systems and data products that accelerate decision-making across the organization.

This position is based in Toronto, ON and is in the office 5 days a week.

Role & Responsibilities:

Forecasting & Machine Learning Engineering

• Design and operationalize enterprise forecasting pipelines and feature layers supporting sales, labor, inventory, and additional predictive domains.
• Build reusable, abstracted ML components that integrate cleanly with RBI’s data ecosystem without embedding forecasting logic in application code.
• Implement ML engineering best practices for time-series and predictive workloads, including versioned data pipelines, feature stores, experimentation scaffolding, and automated drift detection.
• Ensure all predictive outputs are governed, explainable, and linked to authoritative data sources with full lineage.

Data & Platform Engineering

• Own the onboarding, transformation, and modeling of enterprise data into Snowflake to support ML, forecasting, and analytics workloads.
• Design high-performance data models and pipelines optimized for both batch and real-time consumption.
• Optimize data structures for correctness, scalability, and permissioning across multiple business functions and AI applications.

Semantic & AI Layer Enablement

• Support the buildout of RBI’s semantic and AI layers to unlock natural language access to both descriptive and predictive insights.
• Partner with AI Engineering and Functional Data Engineers to define governed semantic models and ensure consistent metric definitions across the enterprise.
• Enable Cortex and approved AI tools with clean, well-structured datasets and metadata to improve accuracy and grounding of AI responses.

Governance, Quality, and Trust

• Implement enterprise governance policies, RBAC frameworks, and documentation to ensure safe and compliant use of data and predictive outputs.
• Develop evaluation datasets, regression tests, and ML validation frameworks to detect drift and ensure reliable performance over time.
• Collaborate with data stewards and security stakeholders to manage PII, enforce quality expectations, and maintain auditability for forecasting and ML workflows.

Cross-Functional Collaboration

• Work with Finance, Operations, Marketing, Technology, and Product teams to enable forecasting capabilities and integrate predictive insights into planning workflows.
• Translate business requirements into scalable ML-ready data assets and semantic models.
• Advocate for data and model architecture best practices that accelerate enterprise AI adoption.

Qualifications & Skills:

• 5+ years in data engineering or analytics engineering with production experience on Snowflake or equivalent cloud data warehouses.
• Strong SQL expertise and advanced data modeling skills (star/snowflake schemas, semantic modeling, metadata management).
• Proficiency in Python and experience operationalizing ML workloads in production.
• Familiarity with ML frameworks such as PyTorch, TensorFlow, Scikit-Learn, or MLlib.
• Experience with time-series forecasting, predictive modeling, or ML feature engineering.
• Experience enabling NLQ or semantic-layer systems (Cortex or similar) is a plus.
• Experience with ELT tooling (e.g., AWS Glue) and data quality or observability frameworks.
• Strong understanding of governance, PII handling, and audit requirements (e.g., SOX-adjacent constraints).

Original Job Description:

The AI / ML Data Engineer at RBI is responsible for building and operationalizing the data and machine learning infrastructure that powers enterprise forecasting and AI-driven insights. This role owns the pipelines, feature layers, and evaluation frameworks required to support scalable forecasting across sales, labor, inventory, and other operational domains, ensuring predictive outputs are accurate, governed, and grounded in source-of-truth enterprise data.

In addition, the role supports the development of RBI’s semantic and AI layers by modeling core datasets and enabling safe, performant natural language access to both descriptive and predictive insights. The AI / ML Data Engineer partners closely with AI Engineering, Data Science, Finance, Operations, and Technology teams to deliver trusted, production-grade ML systems and data products that accelerate decision-making across the organization.

Role & Responsibilities

Forecasting & Machine Learning Engineering

• Design and operationalize enterprise forecasting pipelines and feature layers supporting sales, labor, inventory, and additional predictive domains.
• Build reusable, abstracted ML components that integrate cleanly with RBI’s data ecosystem without embedding forecasting logic in application code.
• Implement ML engineering best practices for time-series and predictive workloads, including versioned data pipelines, feature stores, experimentation scaffolding, and automated drift detection.
• Ensure all predictive outputs are governed, explainable, and linked to authoritative data sources with full lineage.

Data & Platform Engineering

• Own the onboarding, transformation, and modeling of enterprise data into Snowflake to support ML, forecasting, and analytics workloads.
• Design high-performance data models and pipelines optimized for both batch and real-time consumption.
• Optimize data structures for correctness, scalability, and permissioning across multiple business functions and AI applications.

Semantic & AI Layer Enablement

• Support the buildout of RBI’s semantic and AI layers to unlock natural language access to both descriptive and predictive insights.
• Partner with AI Engineering and Functional Data Engineers to define governed semantic models and ensure consistent metric definitions across the enterprise.
• Enable Cortex and approved AI tools with clean, well-structured datasets and metadata to improve accuracy and grounding of AI responses.

Governance, Quality, and Trust

• Implement enterprise governance policies, RBAC frameworks, and documentation to ensure safe and compliant use of data and predictive outputs.
• Develop evaluation datasets, regression tests, and ML validation frameworks to detect drift and ensure reliable performance over time.
• Collaborate with data stewards and security stakeholders to manage PII, enforce quality expectations, and maintain auditability for forecasting and ML workflows.

Cross-Functional Collaboration

• Work with Finance, Operations, Marketing, Technology, and Product teams to enable forecasting capabilities and integrate predictive insights into planning workflows.
• Translate business requirements into scalable ML-ready data assets and semantic models.
• Advocate for data and model architecture best practices that accelerate enterprise AI adoption.

Qualifications & Skills

• 5+ years in data engineering or analytics engineering with production experience on Snowflake or equivalent cloud data warehouses.
• Strong SQL expertise and advanced data modeling skills (star/snowflake schemas, semantic modeling, metadata management).
• Proficiency in Python and experience operationalizing ML workloads in production.
• Familiarity with ML frameworks such as PyTorch, TensorFlow, Scikit-Learn, or MLlib.

Benefits at all of our global offices are focused on physical, mental and financial wellness. We offer unique and progressive benefits, including a comprehensive global paid parental leave program that supports employees as they expand their families, free telemedicine and mental wellness support.

Restaurant Brands International and all of its affiliated companies (collectively, RBI) are equal opportunity and affirmative action employers that do not discriminate on the basis of race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or veteran status, or any other characteristic protected by local, state, provincial or federal laws, rules, or regulations. RBI's policy applies to all terms and conditions of employment. Accommodation is available for applicants with disabilities upon request.