Machine Learning Engineer V, Ads Platform Engineering

What you'll do

• Build high-performance ad serving systems that deliver low-latency, relevant ads while balancing revenue goals and advertiser outcomes.
• Develop real-time inventory forecasting solutions using machine learning models and high-performance server simulations.
• Implement complex ML models for automated bidding, pacing algorithms, and dynamic allocation across direct and programmatic inventory.
• Build scalable infrastructure to manage large volumes of data using big data tools like Spark.
• Create systems for identity resolution and precise behavioral or contextual audience targeting while maintaining user privacy.
• Develop and optimize various ad formats integrated across mobile, web, and television platforms.
• Produce predictive models to forecast campaign effectiveness metrics such as impressions, reach, clicks, and ROI.
• Build simulation solutions to model inventory scenarios including demand fluctuations and pricing strategies.

What we're looking for

• Proficiency in Java, C++, Python, or Scala with a solid understanding of multi-threading and memory management.
• Experience building end-to-end ML model deployment and inference infrastructure for low-latency real-time ad systems.
• Experience handling large volumes of data using big data tools like Spark.
• Experience with yield optimization, scoring, bid ranking models, and dynamic allocation of inventory.
• Ability to model and optimize metrics such as Cost Per Click, Cost Per View, and Cost Per Video.
• Experience building scalable simulation solutions to model inventory scenarios, pricing strategies, and demand fluctuations.
• Knowledge of the advertising marketplace, including publisher-side challenges like fill rates and revenue maximization.
• Ability to collaborate with cross-functional teams to productionize and deploy models at scale.