About the job
At Netflix, our mission is to entertain the world. We launched a new ad-supported tier in November 2022 to offer our members more choice in how they consume their content. The Ads Platform Engineering teams build advertising systems and integrations that powers the delivery of ads using our world class content delivery ecosystem.
Responsibilities
Build state-of-art real-time 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; 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 to unlock spend; Integrate different ad formats available on the platform 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; Modeling and Building Cost Per Click, Cost Per View, and Cost Per Video Complete modeling and optimization; Productionized predictive models to forecast the effectiveness of advertising campaigns, including metrics like impressions, reach, clicks, conversions, and ROI; Building Scalable Simulation solution to model different inventory scenarios, including demand fluctuations, pricing strategies, and inventory allocation; General understanding of the advertising marketplace and landscape, with a focus on publisher side challenges like optimizing fill rates and maximizing revenue in the context of inventory management; Collaborate with cross-functional stakeholders from science team, product, engineering, operations, design, consumer research, etc., to productionize and deploy models at scale
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