Software Engineer Intern, Observability
RokuCardiff, United KingdomPosted 7 April 2026
Tech Stack
Job Description
Teamwork makes the stream work.
Roku 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
role
Are you eager to dive into the world of observability and contribute to building the next generation of Roku's AI-enhanced observability platform? Are you excited to learn about or expand your knowledge of the CNCF open-source ecosystem, including tools for metrics, logs, and tracing, while exploring how AI and Large Language Models can revolutionize observability workflows? Do you enjoy problem-solving, working collaboratively with other teams, and exploring innovative ways to improve their observability experience through intelligent automation? In this role, you'll have the opportunity to grow your skills while helping manage large-scale operations involving high-volume data and requests across multiple regions and clusters, all while pioneering AI driven observability solutions. If you're enthusiastic about learning at the intersection of observability and AI, and ready to make an impact, we'd love to hear from you!
We offer a paid 12-week internship, starting in Summer 2026.
About
the
team
The observability team is an integral part of Roku's central Infrastructure Engineering team, which oversees the service mesh hosting architecture and observability platform that lives on that platform. Together, we are tasked with developing and scaling both the Platform (Kubernetes, Istio, Envoy, operators, etc.) and the Observability stack (OSS/CNCF-supported observability projects). Our goal is to facilitate Roku's shift towards a unified, cloud-agnostic infrastructure where all teams benefit from a common framework with out of the box features.
Within the observability team, we are dedicated to creating a world-class observability platform. We customize and optimize OSS projects to meet our needs and actively contribute to upstream projects, promoting positive changes and engaging with the broader ecosystem.
What
you'll
be
doing :
In this role, you'll take on exciting projects at the cutting edge of AI and observability. Your primary focus will be
exploring
and then making proof of concepts for AI/LLM capabilities within our observability stack. To give some potential examples:
Discovering and evaluating MCP servers
that can be globally hosted to improve the usability and intelligence of our observability platform
Developing AI- driven anomaly detection
systems to proactively identify issues and patterns in our telemetry data
Creating intelligent automation
for common observability workflows using LLMs
Building proof-of-concepts
for AI-assisted troubleshooting and root cause analysis
Explore any other options that benefit our platform
Alongside these AI-focused initiatives, you'll work on smaller tasks to gain hands-on experience with our observability stack, collaborating with a passionate team to solve real-world challenges. Your contributions will directly enhance how Roku monitors, analyzes, and optimizes its systems through the power of AI, giving you a chance to learn, innovate, and make an impact at the forefront of observability technology.
We’re excited if you have:
Currently enrolled in a degree program in Computer Science/Engineering or related field and due to graduate in Dec 2026 ... (truncated, view full listing at source)
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