Machine Learning Engineer 5 - Ads Platform Engineering
Core
Build highly performant, real-time advertising systems and ML models for inventory forecasting, ad decisioning, and yield optimization to monetize Netflix content while ensuring brand safety and member experience.
Role type
Senior IC machine learning engineer (advertising platform)
Builds
Real-time ad serving systems, inventory forecasting solutions, and dynamic allocation algorithms for Connected TV and multi-platform ad delivery.
Domain
Streaming media, Connected TV (CTV), and digital advertising technology
Deliverable
production ML models
Required skills
Java, C++, Python, or Scala; multi-threading and memory management; end-to-end ML model deployment for low-latency real-time systems; big data tools (Spark); yield optimization and bid ranking modeling; cost modeling (CPC, CPV, CPCV); scalable simulation of inventory scenarios.
Preferred skills
Productionizing ML models at scale; experience with ad tech standards (VAST, OpenRTB); publisher-side ad tech systems (ad servers, bidders, yield optimizers, SSPs/DSPs); Lucene indexing; global ads regulations compliance; CTV space constraints.
Technologies
Java, C++, Python, Scala, Spark, Lucene, VAST, OpenRTB
Responsibilities
Build complex ML models for low-latency ad decisioning and dynamic allocation; develop forecasting models for campaign effectiveness; create scalable simulations for inventory scenarios; optimize ad formats and integration with Netflix clients (TV, mobile, web); collaborate with cross-functional teams to deploy models at scale.
Seniority
Senior, hands-on IC
