> Markdown version of [/jobs/ext/2614727-machine-learning-engineer-ii-cnn-digital-products-and-services](https://www.wearedevelopers.com/jobs/ext/2614727-machine-learning-engineer-ii-cnn-digital-products-and-services). 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 II, CNN Digital Products and Services - **Company:** Warner Bros. Discovery - **Location:** New York, NY, United States - **Experience:** Experienced - **Salary:** $112,000.0 - $208,000.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Amazon Web Services, Microsoft Azure, Big Data, Code Review, Encodings, Content Analysis, Continuous Integration, Data Cleansing, Distributed Computing Environment, Information Retrieval, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Standard Sql, Software Construction, Data Streaming, Web Platforms, Feature Engineering, Git, Build Management, Containerization, Scikit Learn, Kubernetes, Information Technology, Machine Learning Operations, Restful APIs, Software Version Control, Data Pipelines, Docker - **Published:** August 23, 2026 - **Apply:** https://www.manhattanjobs.com/job.asp?id=3363349965&tx=FJ4542FFF&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * Graduate degree (MS or PhD) in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative field * 2+ years of professional experience building and deploying machine learning systems in production environments * Strong Python programming skills and experience with machine learning frameworks (e.g., scikit-learn or similar) * Experience across the full ML lifecycle, including data preprocessing, feature engineering, model training, evaluation, and deployment * Solid understanding of software engineering best practices, including version control, testing, and CI/CD * Ability to collaborate effectively with cross-functional partners * Strong communication skills, with the ability to explain technical concepts to non-technical stakeholders Preferred Experience: * Experience working on large-scale consumer internet products (e.g., social, streaming, e-commerce, media) * Hands-on experience with recommendation systems, search, NLP, or information retrieval * Familiarity with data pipelines, feature stores, or embedding infrastructure * Experience with experimentation platforms, A/B testing, and experimentation analysis * Knowledge of cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes) * Interest in generative AI applications and/or the media and news industry Technical Skills: * Languages: Python (required), SQL * ML Frameworks: scikit-learn or similar * Tools: Git, MLflow or similar MLOps tools * Data: Experience working with large datasets, distributed processing, and feature engineering * Deployment: REST APIs, model serving, monitoring, and observability ## Description CNN is seeking a Machine Learning Engineer II to build and deploy ML systems that power personalization, search, recommendations, and content understanding for millions of users across CNN's digital platforms. You will work on production ML systems with measurable product impact, collaborating with cross-functional teams of engineers, data scientists, product managers, and editorial staff., * Build and deploy full-lifecycle machine learning systems in Python for CNN digital products, including personalization, search, recommendations, and content understanding * Develop and maintain production ML pipelines, including feature engineering, model training, evaluation, and serving infrastructure * Implement rigorous experimentation and A/B testing frameworks to validate model performance and product impact * Optimize ML systems for real-time, web-scale performance serving millions of users * Partner with platform and infrastructure teams to ensure ML systems meet reliability, scalability, and performance standards * Contribute to code reviews, documentation, and team knowledge sharing ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Machine Learning for Software Developers (and Knitters)](https://www.wearedevelopers.com/videos/154-machine-learning-for-software-developers-and-knitters) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [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 – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)