R
Roku
Software Engineer (Agentic AI)
Skills
PythonData ScienceTesting & QAPyTorchTensorFlowMachine LearningAI
2 months ago
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Important information
Contract type:
Permanent
Location:
Cambridge, UK
Work mode:
Onsite
Published on:
12 August 2026
What they need
- We are looking for a hands-on, systems-oriented Agentic AI Engineer to design, build, and maintain intelligent agents and copilots that drive automation, accelerate workflows, and unlock new product and platform capabilities for Roku TV
- You will own the full lifecycle of agent development—from prototyping and architecture through orchestration, evaluation, deployment, observability, and continuous improvement
- You will contribute directly to Roku’s AI strategy by engineering reusable components, optimizing agent workflows, and ensuring strong real-world performance in production environments
- Architect, develop, and deploy AI agents and copilots for Roku TV use cases, integrating them with internal systems, tools, and services
- Own end-to-end agentic systems from concept to production, including model selection, prompt and context design, retrieval strategies, backend services, and conversational interfaces
- Design and implement single-agent and multi-agent orchestration patterns, including handoffs, delegation, and cooperative task execution
- Build scalable RAG and context pipelines that provide high-quality grounding for AI systems and keep them aligned with evolving data sources and business logic
- Implement tool-calling, function-calling, and MCP-style integrations so agents can safely take actions and interact with the systems around them
- Create reusable agent templates, modular components, and paved-path patterns that accelerate adoption across teams and use cases
- Establish strong evaluation, observability, and monitoring for conversation quality, task success rate, latency, cost, and overall system performance
- Build safeguards that improve production readiness and reliability, including testing pipelines, controlled rollouts, drift detection, and mechanisms that prevent error amplification in multi-step workflows
- Prototype quickly, run experiments, and translate successful ideas into durable, scalable software solutions
- Partner closely with engineering, product, QA, infrastructure, and cross-functional teams to deliver meaningful business and customer outcomes
Benefits
- Medical, wellness and financial benefits
- Free snacks and access to the company fitness center
- Unlimited paid time off policy
- Work from home opportunities- Experience with observability, evaluation, experimentation, and feedback loops for AI systems in production
- Familiarity with cloud platforms, REST APIs, containerization, and modern deployment environments
- 2+ years of experience in software engineering, AI/ML engineering, backend development, or adjacent domains, with strong software engineering fundamentals and the ability to build production-grade systems
- Bachelor’s or master’s degree in Computer Science, Computer Engineering, Electrical Engineering, Data Science, or a related technical field
- Strong proficiency in Python, plus experience with C/C++ or another systems language
- Experience building and maintaining RAG pipelines, agent frameworks, MCP servers or equivalent function-calling architectures, and conversational interfaces
- Hands-on experience with LLM-based systems, including prompt design, retrieval, tool use, memory handling, and agent orchestration patterns
- Excellent communication skills, sound engineering judgment, and a collaborative working style
- Ability to work independently, manage ambiguity, move quickly, and deliver incrementally in a fast-paced environment
- Experience with multi-agent frameworks or orchestration systems such as LangChain, AutoGen, or Semantic Kernel
- Experience with video, audio, TV, or edge-device environments, especially where latency, cost, and hardware constraints matter
- Familiarity with ML/DL frameworks such as PyTorch or TensorFlow
- Research experience, paper implementation experience, or a habit of applying emerging GenAI techniques pragmatically to real problems
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