MACHINE LEARNING ENGINEER (M/F) (H/F)

About The Company Built for every fandom, Tubi is a leading free streaming service that entertains over 100 million monthly active users worldwide. Offering the world's largest collection of Hollywood movies and TV shows, Tubi also features thousands of creator-led stories and hundreds of Tubi Originals crafted for passionate fans. Headquartered in San Francisco and founded in 2014, Tubi is part of Tubi Media Group, a division of Fox Corporation. The platform is dedicated to providing high-quality, accessible entertainment to diverse audiences, leveraging innovative technology and a vast content library to deliver personalized viewing experiences.

The Machine Learning team at Tubi is at the forefront of innovation, driving the development of personalized user experiences that enhance engagement and satisfaction. With one of the industry's largest content inventories and a broad global user base, the team addresses complex challenges in recommendations, search optimization, content understanding, and ads targeting. We are seeking a highly skilled Staff Machine Learning Engineer to join our team in Toronto. In this role, you will lead the design and implementation of advanced recommendation algorithms and systems that serve a global audience. Your expertise will help refine our personalization strategies, improve system performance, and deliver high-impact machine learning solutions. As a senior technical leader, you will collaborate closely with cross-functional teams including Product, Engineering, and Data Science to develop innovative solutions that drive user engagement and satisfaction.

This is a hybrid role based in Toronto, offering the opportunity to work both remotely and in our office environment.

8+ years of industry experience building production-level machine learning systems

• Master’s degree or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, or a related field
• Experience designing and deploying end-to-end machine learning pipelines, including data extraction, feature engineering, model training, testing, and deployment
• Strong understanding of statistical concepts like hypothesis testing, regression analysis, and performance evaluation metrics for machine learning
• Ability to analyze individual system components and understand the overall architecture of machine learning solutions
• Lead the development, design, and implementation of sophisticated recommendation systems and algorithms tailored for a global user base

Conduct in-depth analysis of algorithmic components, ensuring models are optimized for performance, scalability, and accuracy across multiple regions and product areas

Build and deploy robust machine learning pipelines encompassing data extraction, feature development, model training, testing, and deployment

Continuously monitor and evaluate deployed models, making improvements to meet evolving business goals and enhance user experience

Collaborate with Product, Engineering, and Data Science teams to align on project requirements, set expectations, and deliver innovative machine learning solutions

Stay abreast of industry trends and advancements in machine learning to incorporate best practices into ongoing projects

Competitive salary range of $168,400 - $307,700 CAD, commensurate with experience and skills

Annual discretionary bonus and long-term incentive plans

Comprehensive health benefits including medical, dental, and vision insurance

Paid time off, including a flexible time off policy and parental leave program offering 12 weeks of paid bonding leave

Opportunities for professional development and career growth within a leading entertainment technology company

Tubi is an equal opportunity employer and value diversity in our workforce. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, gender identity, disability, protected veteran status, or any other characteristic protected by law. We also consider qualified applicants with criminal histories in accordance with applicable laws.