Machine Learning Engineer - Ads Platform Engineering

Overview

At Netflix, our mission is to entertain the world. Together, we are writing the next episode — pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.

We launched a new ad-supported tier in November 2022 to offer our members more choice in how they consume their content. Our new tier allows us to attract new members at a lower price point, while also creating a compelling path for advertisers to reach audiences that are deeply engaged.

Our Team

The Ads Platform Engineering teams build advertising systems and integrations that power 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:

• Ads Inventory Management & Forecasting — Real-time inventory forecasting using ML models and high performance ad server simulations. The team also builds systems that enable publisher inventory management solutions, supporting monetization strategies such as dynamic pricing, rate card management, product packaging, inventory split and yield optimization.
• Core Ads Serving — Real-time ad decisioning, delivering relevant, high-quality ads while balancing revenue goals and advertiser outcomes. Build ML models for low-latency environments and manage systems that enhance campaign performance through budgeting, pacing algorithms, and dynamic allocation across direct and programmatic. Develop models for goal-based delivery optimization (e.g., CPC, CPV, CPCV).
• Ads Programmatic — Build interfaces with selected SSPs and DSPs to integrate with advertisers  primary buying mechanisms to unlock spend.
• Ads Member Experience — Build and serve the different ad formats across Netflix clients (TV, mobile app, web) and the ads serving infrastructure. Optimize how ad formats integrate with the Netflix member experience.
• Ads Identity & Audiences — Utilize advanced ML models for identity resolution and precise behavioral and contextual audience targeting. Build foundational systems that deliver relevant ads while upholding privacy. Refine models to drive scale and advertiser outcomes.

Skills & experience we’re seeking:

• 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 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 (impressions, reach, clicks, conversions, ROI)
• Building scalable simulation solutions to model inventory scenarios (demand,…