Software Engineer, Embedded Agentic AI
Roku · Austin, TX
- On site
- Full-time
- $130,000 / year
- Austin, TX
Tailored resume — keyword-matched to this role.
Hiring manager — we find who's hiring.
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Job highlights
- Design and build AI agents for Roku TV.
- Own full lifecycle of agent development.
- Integrate AI agents with internal systems.
- Optimize workflows and ensure production performance.
- Collaborate with cross-functional teams.
About the role
About Roku TV Engineering
Roku TV is where embedded systems, media experiences, and intelligent software come together at massive scale. The Roku TV organization builds technology used on millions of TVs globally, and the team is already applying AI to demanding 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.About the Role
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.What you’ll be doing
- 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.
We’re excited if you have
- Bachelor’s or master’s degree in Computer Science, Computer Engineering, Electrical Engineering, Data Science, or a related technical field.
- 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.
- Strong proficiency in Python, plus experience with C/C++ or another systems language.
- Hands-on experience with LLM-based systems, including prompt design, retrieval, tool use, memory handling, and agent orchestration patterns.
- Experience building and maintaining RAG pipelines, agent frameworks, MCP servers or equivalent function-calling architectures, and conversational interfaces.
- Familiarity with cloud platforms, REST APIs, containerization, and modern deployment environments.
- 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.
- Excellent communication skills, sound engineering judgment, and a collaborative working style.
Nice to have
- 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.
Our Hybrid Work Approach
Roku fosters an inclusive and collaborative environment where teams work in the office Monday through Thursday. Fridays are flexible for remote work except for employees whose roles are required to be in the office five days a week or employees who are in offices with a five day in office policy.Key skills/competency
- Software Engineering
- Embedded Systems
- AI/ML Engineering
- Backend Development
- Python
- C/C++
- LLM Systems
- RAG Pipelines
- Agent Orchestration
- Production Systems
Skills & topics
- Software Engineer
- Embedded AI
- AI Engineer
- Machine Learning Engineer
- Python
- C++
- LLM
- RAG
- Agent Orchestration
- Backend Development
How to get hired
- Tailor your resume: Highlight experience with Python, C/C++, LLM systems, RAG pipelines, and agent orchestration, emphasizing production-grade system development.
- Showcase AI expertise: Detail your hands-on experience with prompt design, retrieval, tool use, memory handling, and agent orchestration patterns.
- Demonstrate systems thinking: Emphasize your ability to architect, build, and maintain scalable, reliable AI systems from concept to production.
- Prepare for technical questions: Be ready to discuss your experience with LLM-based systems, RAG pipelines, conversational interfaces, and observability for AI.
- Highlight collaboration: Showcase your ability to partner effectively with engineering, product, and QA teams to deliver impactful outcomes.
Technical preparation
Master Python for AI agent development.,Practice C/C++ for systems-level programming.,Build RAG pipelines and LLM integrations.,Implement agent orchestration patterns.
Behavioral questions
Describe a complex system you architected.,How do you handle ambiguity in projects?,Share an experience of rapid prototyping.,How do you ensure collaboration with teams?
Frequently asked questions
- What specific AI/ML skills are essential for the Software Engineer, Embedded AI role at Roku?
- For the Software Engineer, Embedded AI position at Roku, essential AI/ML skills include hands-on experience with Large Language Model (LLM) based systems, encompassing prompt design, retrieval strategies, tool use, memory handling, and agent orchestration patterns. Proficiency in building and maintaining RAG pipelines, agent frameworks, and conversational interfaces is also crucial. A strong software engineering foundation, particularly in Python and systems languages like C/C++, is equally important for developing production-grade systems.
- How does Roku approach hybrid work for its Software Engineer, Embedded AI positions?
- Roku employs a hybrid work approach where teams typically work in the office Monday through Thursday. Fridays offer flexibility for remote work, with exceptions for roles that require a full five-day in-office presence or for those in offices with a five-day in-office policy. This structure aims to balance collaborative in-office time with remote flexibility.
- What kind of projects can I expect to work on as a Software Engineer, Embedded AI at Roku?
- As a Software Engineer, Embedded AI at Roku, you will design, build, and maintain intelligent agents and copilots for Roku TV use cases. This involves owning the full lifecycle of agent development, from prototyping and architecture to deployment and continuous improvement. Projects will focus on driving automation, accelerating workflows, and enabling new product and platform capabilities, with an emphasis on integrating these agents with internal systems and ensuring their reliability in resource-constrained environments.
- What are the key technical qualifications for the Software Engineer, Embedded AI role at Roku?
- Key technical qualifications for this role include a Bachelor's or Master's degree in a relevant technical field, 2+ years of software engineering experience, and strong proficiency in Python. Experience with C/C++ or another systems language is also highly valued. Hands-on experience with LLM-based systems, RAG pipelines, agent frameworks, and modern deployment environments (cloud platforms, containerization) is essential.
- How does Roku ensure the reliability and performance of AI systems developed by Software Engineers, Embedded AI?
- Roku emphasizes building safeguards for production readiness and reliability. This includes establishing strong evaluation, observability, and monitoring for AI systems, covering metrics like conversation quality, task success rate, latency, and cost. They also implement testing pipelines, controlled rollouts, drift detection, and mechanisms to prevent error amplification in multi-step workflows.
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