Roku provides the simplest way to stream entertainment to your TV. On your terms. With thousands of available channels to choose from. Backed by Menlo.
About the role
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.
What they're looking for
- Bachelor’s degree or foreign equivalent in Computer Science or related field
- 2 years experience in position, as a Software Engineer, or related occupation
- Build and integrate AI tooling using Agents, Model Context Protocol (or equivalents) with adapters, authentication, auditing, and lifecycle management
- Develop and evaluate prompts and systems with grounding, citation, hallucination mitigation, red-teaming, and A/B experimentation
- Design and deliver user interfaces that prioritize clarity, discoverability, minimal friction, and consistent interaction patterns across surfaces
- Optimize performance through latency/throughput tuning, async/concurrency patterns, profiling, and device–cloud considerations
More about this role
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.
- Responsible for designing and implementing secure, reliable AI-powered tools and platforms—using technologies such as large language models and retrieval-augmented generation—and integrating them into core engineering workflows to improve quality, performance, and operational oversight.
- AI Leadership: Architect and guide implementation of AI tooling and pipelines using LLMs, RAG, and MCP, including retrieval design, model orchestration, safety guardrails,...
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