The posting, in Roku's own words
archived Sep 5, 2026Roku is changing how the world watches TV
Roku is the #1 TV streaming platform in the U.S., Canada, and Mexico, and we've set our sights on powering every television in the world. Roku pioneered streaming to the TV. Our mission is to be the TV streaming platform that connects the entire TV ecosystem. We connect consumers to the content they love, enable content publishers to build and monetize large audiences, and provide advertisers unique capabilities to engage consumers. From your first day at Roku, you'll make a valuable - and valued - contribution. We're a fast-growing public company where no one is a bystander. We offer you the opportunity to delight millions of TV streamers around the world while gaining meaningful experience across a variety of disciplines.
About the team
Roku pioneered streaming to the TV. We connect users to the streaming content they love, enable content publishers to build and monetize large audiences, and provide advertisers with unique capabilities to engage consumers. Roku streaming players and Roku TV™ models are available worldwide through direct retail sales and licensing arrangements with TV brands and pay-TV operators.
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About the role
We seek an outstanding, creative, and passionate Machine Learning engineer to join Roku's Recommendation team. You will be responsible for building and owning the next generation of content recommendations and other algorithms/systems that will make the experience for our many millions of Roku users 100% personalized and unique. How will I use AI at Roku? At Roku, we don’t just use AI, we work with it. We’re looking for an experienced Applied Machine Learning Engineer to build the recommendation intelligence powering next-generation advertising and media planning experiences . This role will develop ML, recommendation, ranking, and optimization capabilities that use advertiser, campaign, product, audience, pricing, inventory, delivery, and performance signals to generate actionable media planning recommendations. What are the responsibilities of the role? Build and productionize recommendation, ranking, and optimization systems using techniques such as collaborative filtering, learning-to-rank, embeddings, similarity modeling, and hybrid approaches Analyze large-scale, heterogeneous datasets to identify meaningful signals and engineer reusable, high-value features that serve multiple models and use cases Develop and evaluate models for batch and real-time prediction that learn from historical and live patterns, defining rigorous metrics for accuracy, ranking quality, diversity, and business impact Design optimization approaches for complex decision and allocation problems, balancing competing objectives against real-world business and operational constraints Build and evolve production ML capabilities across the full model lifecycle, including experimentation, training, versioning, deployment, serving, monitoring, and retraining Build scalable, reliable systems operating on large datasets and distributed infrastructure, troubleshooting complex workloads and optimizing performance Partner with product managers, data scientists, and engineering teams to translate ambiguous business problems into well-defined ML and optimization solutions Use modern AI-assisted development tools effectively and apply advances in ML where they materially improve the product, while maintaining strong engineering judgment and production quality What experience would help someone be successful in this role at Roku? Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, Statistics, Operations Research, or a related field 10+ years of relevant software engineering or experience Strong applied ML experience in production environments Experience building recommendation, ranking, personalization, or decision systems Experience with collaborative filtering, learning-to-rank, embeddings, similarity models, or related approaches Experience with optimization and resource allocation problems Strong Python, Java, and distributed data-processing skills; Spark experience preferred Experience with large-scale ML infrastructure, batch and real-time inference, and low-latency serving Strong judgment in choosing between ML, optimization, heuristics, and rules Ad tech, media planning, campaign optimization, inventory forecasting, or marketplace experience is a strong plus AI growth mindset and demonstrated fluency with modern AI-assisted engineering tools