Machine Learning Engineer 5 - Ads Platform Engineering

The Ads Platform Engineering teams build advertising systems and integrations that powers the delivery of ads using our world class content delivery ecosystem. We use a number of Netflix investments and innovations to power our ads - unique mix of client and server side ad insertions, state of the art content delivery system, ad encoding recipes, content understanding and metadata etc. We deliver ads in a manner that’s thoughtful of our member’s viewing experience and drive great outcomes for advertisers. We also ensure that advertiser brand safety is ensured during serving, members only see the most appropriate ads for them.

Our team is new and yet faced with the enormous ambitions of building highly performant advertising systems and delivering high impact to our business by monetizing our incredible slate of content. As one of the newest entrants in the Connected TV advertising space that’s rapidly growing, we seek to build unique value propositions that help us differentiate from the competition and become a market leader in record time.

We are looking for highly motivated engineers working in the advertising space who are excited to join us on this journey.

We are hiring for multiple roles across our Ads Platform teams. As you progress through the interview process, you will be assessed for the following roles:

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.

The Core Ads Serving team powers real-time ad decisioning, delivering relevant, high-quality ads while balancing revenue goals and advertiser outcomes. They 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. Additionally, the team develops models for goal-based delivery optimization, such as CPC, CPV, and CPCV.