Job Description
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.
Location Type:
Hybrid
Location:
Toronto, ON
OVERVIEW OF THE COMPANY
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.
ABOUT THE ROLE
As a Staff ML Engineer at AdRise, you’ll play a hands‑on leadership role in building and scaling our machine learning infrastructure. You’ll design real‑time ML systems that power auction dynamics, yield optimization, identity resolution, and forecasting—all under strict latency constraints. Working alongside the Director of ML and a growing team, you’ll help shape the technical roadmap and bring new models to life in production environments at massive scale.
A SNAPSHOT OF YOUR RESPONSIBILITIES
Design and deploy low‑latency ML pipelines for real‑time bidding and ad delivery
Build scalable systems for forecasting, yield optimization, and media planning
Collaborate with data engineering teams to integrate third‑party data and behavioral signals
Drive experimentation through A/B testing and continuous model optimization
Define architecture standards and tooling for MLOps, retraining workflows, and model monitoring
Mentor senior and junior engineers across ML engineering best practices
Evaluate third‑party tools and platforms to inform build‑vs‑buy decisions
Own performance metrics tied to business impact: eCPM, ROAS, fill rate, etc.
WHAT YOU WILL NEED
8+ years in ML or data engineering roles, with at least 2+ years in high‑scale, real‑time systems
Proficiency in Python, TensorFlow/PyTorch, and production ML tooling (e.g., Airflow, Spark, MLflow)
Experience deploying ML systems with sub‑100ms latency in adtech or streaming contexts
Strong background in MLOps: retraining workflows, model versioning, pipeline orchestration
Familiarity with AWS, Kubernetes, and CI/CD frameworks
Ability to translate business goals into measurable model outcomes
NICE TO HAVE, BUT NOT A DEALBREAKER
Prior experience in adtech or AVOD platforms
Familiarity with identity resolution, lookalike modeling, and auction theory
Exposure to tools like Snowflake, LiveRamp, or AWS Clean Rooms
#LI-Hybrid
EMPLOYEE BENEFITS
Vacation and sick time (US‑based non‑exempt employees)
Flexible time‑off policy for salaried/exempt employees (in lieu of FOX vacation policy)
Generous parental leave program (12 weeks of paid bonding leave)
Monthly wellness reimbursement
Equal Opportunity Statement:
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.
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