Machine Learning Engineer

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 growing to meet ambitious goals: building highly performant advertising systems to effectively monetize our incredible slate of content and deliver real impact for the business. 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.

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. We create foundational systems that deliver relevant and engaging ads to Netflix members, all while upholding their privacy. Our continuous refinement of models generates a flywheel effect, enhancing member experiences and driving optimal advertiser outcomes at scale.

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

Skills  experience we’re seeking:

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

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.