Senior Engineering Manager - Machine Learning

What does the team work on?

Roku pioneered streaming to the TV and continues to innovate and lead the industry. While we are well-positioned to help shape the future of television – including TV advertising – around the world, continued success relies on its investment in our machine learning capabilities. Roku offers millions of options to our users: movies, episodes, 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 / AI Engineering Manager, Content Platform who can drive further innovation.

What is the role?

Roku is seeking a Senior Machine Learning / AI Engineering Manager, Content Platform to lead and advance machine learning capabilities within the Content Platform organization. This role is responsible for defining the ML roadmap, guiding model architecture and MLOps infrastructure, and ensuring that ML systems are scalable and production-ready. The Senior ML / AI Engineering Manager will lead a high-performing team of ML Engineers, driving career development, performance management, and a culture of rigorous experimentation. This individual will partner cross-functionally with Product, Engineering, and Content Strategy to align ML initiatives with business goals and deliver measurable improvements to the production environment. The ideal candidate combines deep technical ML expertise with strong organizational leadership, serving as the architectural and operational bridge between AI research and fast-paced product delivery. This role also involves integrating Generative AI capabilities and Vision Models to enrich content quality and user engagement across the Roku ecosystem.

How will I use AI at Roku?

At Roku, we don’t just use AI, we work with it. We’re looking for curious, adaptable builders who can show how they’ve used AI or automation to move faster, raise the bar, and scale their impact. Ideally, you have built fluency across the agentic engineering toolchain — coding harnesses like Claude Code or Cursor, MCP servers, custom skills, or agent frameworks. And you can describe projects where you shipped real work with these tools.

What are the responsibilities of the role?

• Define and own the roadmap for ML within the Content Platform, establishing a multi-year vision and translating it into iterative delivery cycles
• Provide high-level guidance on model architecture and MLOps infrastructure to ensure models are scalable and production-ready
• Lead and grow a high-performing team of ML Engineers, including career development, performance management, and fostering a culture of rigorous experimentation
• Partner with Product, Engineering, and Content Strategy to align ML initiatives with business goals
• Champion a culture of incremental delivery, balancing long-term R&D with the need to ship measurable improvements to the production environment
• Integrate Generative AI capabilities and Vision Models to enrich content quality and user engagement
• Drive the development of a content knowledge platform that provides insights to downstream systems such as Search, Recommendations, Ads, and Voice

What experience would help someone be successful in this role at Roku?

• 15+ years of total experience in software or ML engineering, with at least 5 years in a dedicated people management leadership role
• Deep theoretical and practical understanding of Machine Learning
• Understanding of the streaming business and how ML directly impacts business outcomes
• Ability to communicate complex model concepts to non-technical stakeholders as effectively as debating architecture with experienced engineers
• 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)
• MS or BS in Computer Science, Mathematics, or a related quantitative field

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