Software Engineer (Embedded Agentic AI)

• Roku TV is where embedded systems, media experiences, and intelligent software come together at a massive scale. The Roku TV organization develops technology used on millions of TVs worldwide, and the team is already applying AI to challenging TV problems in resource-constrained environments where quality, performance, and reliability matter
• As part of this team, you will help define how agentic AI systems are designed, built, and operated for Roku TV use cases. This is a hands-on engineering role for someone who treats AI agent design as an engineering discipline: architecting durable systems, grounding them in the right context, integrating them with tools and services, and making them reliable in production
• 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 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
• Familiarity with cloud platforms, REST APIs, containerization, and modern deployment environments
• 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
• 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
• Experience with observability, evaluation, experimentation, and feedback loops for AI systems in production
• Ability to work independently, manage ambiguity, move quickly, and deliver incrementally in a fast-paced environment
• Research experience, paper implementation experience, or a habit of applying emerging GenAI techniques pragmatically to real problems
• Familiarity with ML/DL frameworks such as PyTorch or TensorFlow
• Experience with video, audio, TV, or edge-device environments, especially where latency, cost, and hardware constraints matter
• Experience with multi-agent frameworks or orchestration systems such as LangChain, AutoGen, or Semantic Kernel