Machine Learning Engineer 5 - Ads Inventory Management & Forecasting

Netflix is a company focused on entertaining the world through innovative storytelling and technology. They are seeking a Senior Machine Learning Engineer to join their Ads Inventory Management & Forecasting team, which builds real-time inventory forecasting solutions using ML models and ad server simulations.

Responsibilities:

• 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
• Collaborate with cross-functional stakeholders from science team, product, engineering, operations, design, consumer research, etc., to productionize and deploy models at scale

Requirements:

• 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
• Collaborate with cross-functional stakeholders from science team, product, engineering, operations, design, consumer research, etc., to productionize and deploy models at scale
• 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