Senior Machine Learning Engineer, Content Platform

About the Team

Roku offers millions of options to our users: movies, shows, news, sports, and channels from all around the world. The Roku Content Platform is key to onboarding content into the Roku ecosystem, delighting our customers. Building a content knowledge platform that provides insights to downstream systems like Search, Recommendations, Ads, and Voice to shape customers' experiences is why we’re actively looking for a Senior Machine Learning Engineer who can drive further innovation.

About the Role

The person in this role will leverage their technical skills, business intuition, and analytical thinking to build best-in-class machine learning products. This includes integrating Generative AI capabilities and Vision Models to enrich content quality and user engagement. This is a role for a hardened ML practitioner and a highly skilled organizational leader. You will be the architectural and operational bridge between deep AI research and fast-paced product delivery. If you know how to build a 3-year vision for content discovery and translate it into 2-week shipping cycles, this is your team.

What You’ll Be Doing

• Vision & Strategy: Define the roadmap for ML within the Content Platform
• Technical Leadership: Provide high-level guidance on model architecture and MLOps infrastructure to ensure models are scalable and production-ready
• Cross-Functional Execution: Partner with Product, Engineering, and Content Strategy to align ML initiatives with business goals
• Delivery: Champion a culture of incremental delivery. Balance long-term R&D with the need to deliver measurable improvements to the production environment

We’re Excited If You Have

• 10+ years of total experience in software/ML engineering
• MS or BS in Computer Science, Mathematics, or a related quantitative field
• Deep theoretical and practical understanding of Machine Learning
• You understand the business of streaming and how ML directly impacts the bottom line
• You can explain complex models to a non-technical executive just as easily as you can debate architecture with a seasoned engineer
• Proven track record of deploying large-scale ML models into production environments
• Experience with modern ML stacks (e.g., LLMs, PyTorch, TensorFlow, Spark, and cloud-native MLOps tools)