About the job
Machine Learning Engineer 5 - Ads Inventory Management & Forecasting at Netflix, building state-of-the-art real-time inventory forecasting solutions leveraging ML models and high-performance ad server simulations, and systems for publisher inventory management supporting monetization strategies.
Responsibilities
Build end-to-end ML model deployment and inference infra for low-latency real-time ad systems.
Productionize predictive models to forecast advertising campaign effectiveness (impressions, reach, clicks, conversions, ROI).
Build scalable simulation solutions to model inventory scenarios including demand fluctuations, pricing strategies, and inventory allocation.
Collaborate with cross-functional stakeholders (science team, product, engineering, operations, design, consumer research) to productionize and deploy models at scale.
Qualifications
Minimum
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.
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.
Preferred
Good understanding of Lucene index and had experience building Lucene index with large volume of data.
Familiar with publisher-side ad tech systems including ad servers, bidders, yield optimizers, and their demand-side counterparts (SSPs/DSPs).
Experience in yield optimization, product recommendation and dynamic allocation of direct/programmatic guaranteed and non-guaranteed inventory.
Contributed to an ads industry technology standard (e.g. VAST, OpenRTB) or worked on an industry consortium effort, working group etc.
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.