> Markdown version of [/jobs/ext/552304-machine-learning-engineer-5-ads-platform-engineering](https://www.wearedevelopers.com/jobs/ext/552304-machine-learning-engineer-5-ads-platform-engineering). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer 5 - Ads Platform Engineering - **Company:** Netflix, Inc. - **Location:** Los Gatos, United States (Remote available) - **Contract:** Permanent contract - **Skills:** Java (Programming Language), C++ (Programming Language), Apache Lucene, Memory Management, Mobile Application Software, Python (Programming Language), Machine Learning, Automation of Marketing, Multithreading, Apache Spark, Yield Optimization, Data Management, Machine Learning Operations - **Published:** June 15, 2026 - **Apply:** https://www.dice.com/job-detail/7ba0a610-259e-430e-9aec-264ad58f1350 ## About the Role We are looking for highly motivated engineers working in the advertising space who are excited to join us on this journey., * 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 guaranteed and non-guaranteed inventory * Modeling and Building Cost Per Click, Cost Per View, and Cost Per Video Complete modeling and optimization, * Experience in productionizing ML models and deploying models at scale. * Contributed to an ads industry technology standard (e.g VAST, OpenRTB) or worked on an industry consortium effort, working group etc. * Familiar with publisher-side ad tech systems including ad servers, bidders, yield optimizers, and their demand-side counterparts (SSPs/DSPs) * Good understanding of Lucene index and had experience building Lucene index with large volume of data. * 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 ## Description * 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. * The Ads Programmatic team builds interfaces with selected SSPs and DSPs to integrate with Advertisers' primary buying mechanisms to unlock spend. * The Ads Member Experience team is responsible for building and serving the different ad formats available on the platform. The team owns the integration between the different Netflix clients (TV, mobile app, web) and the ads serving infrastructure. One of its primary goals is to optimize how different ad formats are integrated with the Netflix member experience. * The Ads Identity & Audiences team is revolutionizing ad experiences by utilizing advanced machine learning models for identity resolution and precise 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., * 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 ## Related Videos - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Asynchronicity and multithreading in C#](https://www.wearedevelopers.com/videos/447-asynchronicity-and-multithreading-in-c) - [Writing a full-text search engine in TypeScript](https://www.wearedevelopers.com/videos/504-writing-a-full-text-search-engine-in-typescript) - [DevOps at Netflix](https://www.wearedevelopers.com/videos/270-devops-at-netflix) - [Multithreading in Javascript: A guide to Web Workers](https://www.wearedevelopers.com/videos/1673-multithreading-in-javascript-a-guide-to-web-workers) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How Much FAANG Companies Actually Pay Software Engineers in 2025](https://www.wearedevelopers.com/magazine/230-how-much-faang-companies-actually-pay-software-engineers-in-2025) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [How machine learning can help us tell fact from fiction](https://www.wearedevelopers.com/magazine/509-how-machine-learning-can-help-us-tell-fact-from-fiction) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)