Director, ML Engineering

Tubi - Canada•Toronto, ON

2d•CA$272,900 - CA$389,900•Hybrid

About The Position

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 Director of Machine Learning Engineering to lead and build a high-performing team while staying close to the technical work. This is a hands-on leadership role: you will set the strategic direction for our recommendation, personalization, and ads optimization systems, including recommendation foundation modeling, and you will stay in the algorithms and the code, conducting deep dives into modeling components and guiding the hardest technical decisions yourself. You will tackle complex machine learning problems at scale and partner closely with cross-functional teams to ship solutions that measurably improve how hundreds of millions of viewers experience Tubi.

Requirements

10+ years of industry experience in machine learning, with a strong record of building and deploying end-to-end ML systems at scale.
3+ years of leadership and management experience, with a proven ability to build and lead strong technical teams while remaining technically hands-on.
MSc or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, or a related field, or equivalent practical experience.
Deep expertise in deep learning for recommendation and personalization systems, including hands-on experience with TensorFlow, PyTorch, or similar frameworks.
Proficiency in building and deploying full-stack ML pipelines: data extraction, data mining, feature development, model training, testing, and deployment.
Solid command 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 while holding the overall architecture of ML solutions in view.
Excellent communication skills and the ability to influence product and technical strategy.

Responsibilities

Lead, build, and grow a high-performing ML engineering team, fostering a culture of technical excellence, ownership, and rapid iteration.
Set and execute the strategic roadmap for recommendation, personalization, and ads optimization systems, including recommendation foundation modeling for a global audience.
Stay hands-on: conduct deep dives into algorithmic components and systems, ensuring models are optimized for both performance and scalability across regions and product areas.
Lead the design, development, and implementation of advanced recommendation systems and algorithms, contributing directly to the most challenging technical problems.
Build and deploy robust, full-stack ML pipelines: data extraction, feature development, model training, testing, deployment, and serving.
Continuously monitor, evaluate, and optimize the performance of deployed models, ensuring they meet business goals and deliver high-quality user experiences.
Partner closely with Product, Engineering, and Content teams to align on requirements, set expectations, and deliver ML-driven solutions that improve user engagement.