Machine Learning Engineer 5 - Ads Signals & Targeting

Netflix•,

30d•$466,000 - $750,000

About The Position

Netflix launched a new ad-supported tier in November 2022 to offer members more choice in how they consume their content. This tier allows Netflix to attract new members at a lower price point, while also creating a compelling path for advertisers to reach audiences that are deeply engaged. The Ads Platform Engineering teams build advertising systems and integrations that power the delivery of ads using Netflix's world-class content delivery ecosystem. They use a unique mix of client and server side ad insertions, state-of-the-art content delivery system, ad encoding recipes, content understanding and metadata to deliver ads in a manner that’s thoughtful of the member’s viewing experience and drives great outcomes for advertisers. They also ensure advertiser brand safety during serving and that members only see the most appropriate ads for them. The Ads Signals & Targeting team is revolutionizing ad experiences by utilizing advanced machine learning models for identity resolution and optimal behavioral and contextual audience targeting. They create foundational systems that deliver relevant and engaging ads to Netflix members, all while upholding their privacy. Their continuous refinement of models generates a flywheel effect, enhancing member experiences and driving optimal advertiser outcomes at scale. The team is looking for highly motivated engineers working in the advertising space who are excited to join them on this journey.

Requirements

Proficiency in Java, C++, Python, or Scala with a solid understanding of multi-threading and memory management.
Experience in handling data at extremely large volumes with big data tools like Spark.

Nice To Haves

Strong understanding of data privacy, governance, and security concepts, including Privacy by Design principles.
Experience with ads targeting using machine learning models.
Experience working in the CTV space and knowledge of its unique constraints.

Responsibilities

Experience in building end-to-end ML model deployment and inference infra for low-latency real-time ad systems.
Experience building machine learning models for ads targeting and lookalike expansion.
Professional experience in designing and building user and contextual signals and audience targeting features for ad platforms.
Collaborate with cross-functional stakeholders from the science team, product, engineering, operations, design, consumer research, etc., to productionize and deploy models at scale