Principal Machine Learning Engineer
@ Tubi

The Machine Learning team at Tubi drives the innovation behind personalized user experiences for millions of viewers worldwide. From enhancing recommendations and search to content understanding and ads optimization, our team tackles large-scale challenges that shape the future of streaming.

We are seeking a highly skilled Principal 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.

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 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 Content teams to align on product requirements, set expectations, and deliver machine learning-driven solutions that improve user engagement.

Your Background:

• 10+ years of industry experience in Machine Learning.
• MSc or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, or a related field.
• Expertise in deep learning technologies for recommendation systems, including experience with 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.