Senior Machine Learning Engineer (Toronto)

Fox Corporation

9d•Hybrid

About The Position

AdRise is FOX's core ad tech platform, built to unify streaming and linear advertising across Tubi, FOX Sports, FOX News Media, and FOX Entertainment. Before AVOD became industry standard, AdRise helped power Tubi's growth from the ground up by developing real-time ad delivery and dynamic targeting infrastructure that scaled to serve millions. That same tech foundation is now fueling FOX's cross-portfolio monetization strategy. We are rebuilding the ad stack for video by optimizing server-side delivery, building ML-driven targeting systems, and enabling content-personalized ad experiences that respond to what viewers actually watch. From real-time decisioning to data collaboration with AWS and Snowflake, we are creating a smarter, more dynamic ad tech ecosystem. The problems are complex, the visibility is high, and the platform is built for impact. If you want to work at the center of streaming monetization on systems that touch billions of impressions and shape the next generation of personalized advertising, this is your platform. As a Senior ML Engineer at AdRise, you will develop, deploy, and maintain scalable machine learning systems that directly impact monetization across FOX’s streaming and broadcast properties. This is a highly technical and hands-on role focused on building real-time inference pipelines, training infrastructure, and experimentation frameworks that fuel next-gen ad personalization and optimization. You’ll work in close collaboration with data science, engineering, and product teams to deploy ML solutions that operate under strict performance constraints.

Requirements

5+ years of hands-on machine learning or data engineering experience
Proficiency with Python and ML frameworks such as TensorFlow or PyTorch
Experience with real-time inference systems and streaming data architectures
Familiarity with AWS, Spark, Kubernetes, and cloud-native development practices
Background in deploying ML systems in production environments with measurable impact
Solid understanding of ML experimentation, evaluation metrics, and optimization techniques

Nice To Haves

Experience working in adtech, streaming, or real-time bidding environments
Familiarity with yield optimization, media planning, or auction modeling
Exposure to tools like MLflow, Snowflake, or Airflow

Responsibilities

Design and maintain machine learning pipelines for real-time ad decisioning
Work with cross-functional teams to translate business goals into model solutions
Implement experimentation systems (A/B testing, multi-armed bandits) to continuously optimize outcomes
Collaborate on data integration with partners such as LiveRamp and TransUnion
Contribute to the team’s MLOps infrastructure, from model training to deployment and monitoring
Participate in code reviews, system design discussions, and sprint planning
Conduct performance tuning and latency optimization for production ML systems