Machine Learning Engineer II, Burger King, US&C

Mission

Join us on our journey to achieve our big dream of building the most loved restaurant brands in the world. 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®.

About the Role

As a Machine Learning Engineer II, you will be responsible for developing and iterating machine learning models that drive measurable improvements in restaurant performance, including traffic and profitability, on at scale. This role focuses on transforming large-scale transactional and operational data into predictive and prescriptive models that power data-driven decision systems across the Burger King U.S. & Canada business.

Responsibilities

• Design, develop, and iterate on machine learning models, including causal inference, recommendation systems, clustering, and optimization models to address high-impact business problems.
• Experimentation & Impact Evaluation: Partner with Analytics Engineering to design and evaluate experiments (e.g., A/B testing, matched cohorts, difference-in-differences) to validate model performance and quantify real-world impact.
• Optimization & Decision Modeling: Develop models that inform actionable decisions, including prioritization frameworks and expected value-based optimization to drive improvements in traffic and profitability.
• Model Evaluation & Continuous Improvement: Monitor, evaluate, and refine model performance using statistical methods, backtesting, and iterative experimentation to ensure accuracy, stability, and sustained impact.
• Feature Engineering from Curated Data: Transform curated datasets into high-quality model inputs through feature engineering, selection, and validation, leveraging domain knowledge and statistical techniques.
• Collaboration with Engineering & MLOps: Work closely with Analytics Engineering, Data Engineering, and MLOps teams to ensure models are production-ready, scalable, and effectively integrated into downstream systems.

Requirements

• 3+ years of experience in machine learning, applied statistics, or a related field, with a focus on developing and evaluating models in real-world applications.
• Bachelor’s or Master’s degree in Statistics, Economics, Operations Research, Mathematics, Computer Science, or a related quantitative field; equivalent applied experience will also be considered.
• Strong foundation in statistical modeling and machine learning, with the ability to explain model selection, assumptions, and trade-offs.
• Experience applying a range of modeling techniques such as regression, clustering, recommendation systems, and optimization methods.
• Familiarity with experimental design and causal inference techniques (e.g., A/B testing, difference-in-differences, cohort-based analysis).
• Strong programming skills in Python for analysis and model development.
• Proficiency in SQL and experience working with large-scale datasets in Snowflake or similar cloud data warehouses.
• Experience working in AWS environments (e.g., SageMaker, EMR) and familiarity with workflow orchestration tools such as Dagster or Airflow.

Qualifications

• Strong foundation in statistical modeling and machine learning, with the ability to explain model selection, assumptions, and trade-offs.
• Experience applying a range of modeling techniques such as regression, clustering, recommendation systems, and optimization methods.
• Familiarity with experimental design and causal inference techniques (e.g., A/B testing, difference-in-differences, cohort-based analysis).
• Strong programming skills in Python for analysis and model development.
• Proficiency in SQL and experience working with large-scale datasets in Snowflake or similar cloud data warehouses.
• Experience working in AWS environments (e.g., SageMaker, EMR) and familiarity with workflow orchestration tools such as Dagster or Airflow.

Skills

• Strong foundation in statistical modeling and machine learning, with the ability to explain model selection, assumptions, and trade-offs.
• Experience applying a range of modeling techniques such as regression, clustering, recommendation systems, and optimization methods.
• Familiarity with experimental design and causal inference techniques (e.g., A/B testing, difference-in-differences, cohort-based analysis).
• Strong programming skills in Python for analysis and model development.
• Proficiency in SQL and experience working with large-scale datasets in Snowflake or similar cloud data warehouses.
• Experience working in AWS environments (e.g., SageMaker, EMR) and familiarity with workflow orchestration tools such as Dagster or Airflow.

Benefits

RBI's 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.

Pay

The salary range for this position is $80,000 - $120,000 annually, depending on experience and qualifications.

Schedule

RBI follows a 5 day, in-office work schedule to support collaboration. Candidates should be comfortable working onsite 5 days per week out of our office in Miami, FL.