Staff Machine Learning Engineer

Boldly built for every fandom, Tubi is a free streaming service that entertains over 100 million monthly active users. Tubi offers the world's largest collection of Hollywood movies and TV shows, thousands of creator-led stories and hundreds of Tubi Originals made for the most passionate fans. Headquartered in San Francisco and founded in 2014, Tubi is part of Tubi Media Group, a division of Fox Corporation.

About the Role:

The Machine Learning team at Tubi drives the innovation behind personalized user experiences.  With the largest inventory in the industry and hundreds of millions of viewers, we tackle problems in the space of recommendations, search, content understanding and ads optimization that shape the future of streaming.

We are seeking a highly skilled Staff Machine Learning Engineer to contribute to transformative projects in video personalization. In this role, you will design and implement advanced algorithms and systems to improve our personalization strategy. As a senior technical expert, you will tackle complex problems in machine learning at scale, collaborating closely with cross-functional teams to develop and optimize machine learning-driven solutions.

This is a hybrid role in our Toronto office.

What You'll Do:

• Lead the design, development, and implementation of advanced recommendation systems and algorithms for a global audience
• Conduct deep dives into algorithmic components and systems, ensuring that models are optimized for both performance and scalability across multiple regions and product areas
• Build and deploy high-impact robust ML pipelines, including data extraction, feature development, model training, testing, and deployment
• Continuously monitor, evaluate, and optimize the performance of deployed models, ensuring they meet business goals and provide high-quality user experiences.
• Work closely with Product, Engineering, and Data Science teams to align on product requirements, set expectations, and deliver machine learning-driven solutions that improve user engagement

Your Background:

• 8+ years of industry experience building production Machine Learning systems
• MSc or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, or a related field
• Experience with deep learning technologies for recommendation systems, including TensorFlow, PyTorch, or similar frameworks
• Proficiency in building and deploying full-stack machine learning pipelines: data extraction, data mining, model training, feature development, testing, and deployment.
• Solid understanding of statistical concepts such as hypothesis testing, regression analysis, and performance evaluation metrics for machine learning.
• Ability to deep dive into individual components and systems, as well as understand the overall architecture of machine learning solutions.

#LI-Hybrid

Tubi is a division of Fox Corporation, and the FOX Employee Benefits summarized here, covers the majority of all US employee benefits.  The following distinctions below outline the differences between the Tubi and FOX benefits:

• For US-based non-exempt Tubi employees, the FOX Employee Benefits summary accurately captures the Vacation and Sick Time.
• For all salaried/exempt employees, in lieu of the FOX Vacation policy, Tubi offers a Flexible Time off Policy to manage all personal matters.
• For all full-time, regular employees, in lieu of FOX Paid Parental Leave, Tubi offers a generous Parental Leave Program, which allows parents twelve (12) weeks of paid bonding leave within the first year of birth, adoption, surrogacy, or foster placement of a child in addition to applicable government leave program(s) and FOX's short-term disability policy. This time is 100% paid through a combination of any applicable state, city, and federal leaves and wage-replacement programs in addition to contributions made by Tubi.
• For all full-time, regular employees, Tubi offers a monthly wellness reimbursement.

We are an equal opportunity employer and 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 will consider for employment qualified applicants with criminal histories consistent with applicable law.