Machine Learning Engineer III, Tim Hortons

Restaurant Brands International•Toronto, ON

30d•CA$100,000 - CA$130,000•Onsite

About The Position

Restaurant Brands International Inc. (RBI) is seeking an experienced Machine Learning Engineer to join its Advanced Analytics team. This team is responsible for the end-to-end lifecycle of machine learning models, including building, training, deploying, scoring, and monitoring for various applications such as personalized recommendations, forecasting, and Large Language Models (LLMs). The role is pivotal in advancing the company's capabilities in advanced analytics and machine learning. RBI emphasizes a 5-day in-office work schedule to foster collaboration, and this position requires working onsite at the Toronto, ON office.

Requirements

Bachelor’s or advanced degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field.
3+ years of experience designing, building, and operating production-scale machine learning systems as a Machine Learning Engineer, Senior Data Scientist, or similar role.
Expert-level programming in Python with extensive experience using PySpark and distributed data platforms (e.g., Databricks) to process and model large-scale datasets.
Proven ability to architect and rebuild complex ML frameworks from the ground up, incorporating multi-threaded processing, distributed workloads, and coordinated synchronous/asynchronous task management to support scalable data and model pipelines.
Deep understanding of distributed data processing frameworks, including Spark execution model, partitioning strategies, shuffle optimization, worker-level execution, and advanced techniques such as Pandas UDFs, vectorized processing, and large-scale performance tuning.
Strong experience developing and productionizing models using frameworks such as Scikit-Learn, MLlib, and PyTorch, with solid understanding of regression, time series, clustering, supervised/unsupervised learning, and deep learning.
Proven experience owning production ML systems, including participating in model on-call rotations, monitoring model performance and data distribution, troubleshooting incidents, and maintaining model reliability.
Strong SQL expertise for large-scale data manipulation and feature engineering.
Experience implementing MLOps / LLMOps practices and deploying ML workloads to cloud environments such as AWS (EC2, S3, DynamoDB, Lambda).

Nice To Haves

Take ownership and manage strategic initiatives in a rapidly evolving QSR environment.
Eagerness to learn and adapt in ambiguous problem spaces with a collaborative attitude.
Collaborate with engineering and product teams to implement ML and GenAI solutions in production.
Focus on outcomes, analyze and visualize data to drive continuous improvement across the business.
Work effectively in a fast-paced Agile environment, maintaining transparency and professionalism in communication.

Responsibilities

Develop and deploy Machine Learning, Deep Learning and GenAI models to enhance operational efficiency and customer experience.
Continuously improve models by monitoring performance, conducting A/B testing, and implementing feedback loops.
Stay updated with the latest advancements in machine learning and AI to ensure our solutions remain cutting-edge.
Collaborate with cross-functional teams including engineers and product managers to integrate AI solutions into production systems.