About the job
We are hiring for multiple roles across our Ads Platform teams. The Ads Inventory Management & Forecasting team builds state-of-art realtime inventory forecasting solution leveraging ML models and high performance ad server simulations. The team also builds systems that enable publisher inventory management solutions, which supports various monetization strategies such as dynamic pricing, rate card management, product packaging, inventory split and yield optimization.
Responsibilities
Build state-of-art realtime inventory forecasting solution leveraging ML models and high performance ad server simulations.
Build systems that enable publisher inventory management solutions, which supports various monetization strategies such as dynamic pricing, rate card management, product packaging, inventory split and yield optimization.
Power real-time ad decisioning, delivering relevant, high-quality ads while balancing revenue goals and advertiser outcomes.
Build complex ML models for low-latency environments and manage core systems that enhance campaign performance through budgeting, pacing algorithms, and dynamic allocation across direct and programmatic.
Develop models for goal-based delivery optimization, such as CPC, CPV, and CPCV.
Build interfaces with selected SSPs and DSPs to integrate with Advertisers' primary buying mechanisms.
Build and serve the different ad formats available on the platform; own integration between Netflix clients (TV, mobile app, web) and ads serving infrastructure.
Optimize how different ad formats are integrated with the Netflix member experience.
Utilize advanced machine learning models for identity resolution and precise behavioral and contextual audience targeting.
Create foundational systems that deliver relevant and engaging ads to Netflix members, all while upholding their privacy.
Qualifications
Minimum
Proficiency in Java, C++, Python, or Scala with a solid understanding of multi-threading and memory management
Experience in building end-to-end ML model deployment and inference infra for low-latency real-time ad systems.
Experience in handling data at extremely large volumes with big data tools like Spark.
Yield Optimization, scoring, and bid ranking models, and Dynamic Allocation of direct/programmatic guaranteed and non-guaranteed inventory
Preferred
Experience in productionizing ML models and deploying models at scale.
Contributed to an ads industry technology standard (e.g VAST, OpenRTB) or worked on an industry consortium effort, working group etc.
Familiar with publisher-side ad tech systems including ad servers, bidders, yield optimizers, and their demand-side counterparts (SSPs/DSPs)
Good understanding of Lucene index and had experience building Lucene index with large volume of data.
Familiarity with legal compliance and changing landscape of ads regulations around the world.
Experience working in the CTV space and knowledge of its unique constraints