Sr. SW Engineer, Machine Learning

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 Roku Roku is the leading TV streaming platform in the US, with a mission to connect the entire TV ecosystem by helping consumers discover content, enabling publishers to grow audiences, and giving advertisers powerful ways to engage viewers. About the Team The Ad Engineering team builds the platform behind Roku's advertising business. Within Ad Engineering, the Business Applications team owns the systems and tooling that power the advertising revenue lifecycle. The platform internal teams and advertisers rely on to plan, price, book, and grow campaigns at scale. About the Role We are hiring a Senior Engineer to lead the architecture and implementation of a next-generation, agent-native, AI-powered business applications platform. This is a high-impact role focused on building intelligent, recommendation-driven experiences that make day-to-day revenue decisions with pricing, booking, upsells, retention, and media planning faster, smarter, and more accessible to the teams that run Roku's advertising business. You will architect and build the end-to-end system for AI-driven recommendations and decisioning, from business and customer signals through to actionable, validated outputs. You will work closely with ML, backend, frontend, data, and business stakeholders to deliver a production-ready platform that improves speed, accuracy, and scalability across the advertising revenue workflow. What You'll Do • Define the technical architecture and overall stack for an agent-native business applications platform spanning pricing guidance, booking and order intelligence, upsell recommendations, churn and retention prediction, media planning, deal scoring, revenue forecasting, and more. • Evaluate LLMs, multimodal systems, multi-agent orchestration frameworks, and recommendation, ranking, and forecasting models for product use. • Design and build the pipeline from business and customer signals to model inference, recommendation generation, output validation, and integration with internal revenue systems and APIs. • Build production-grade systems with strong error handling, output validation, explainability, auditability, and human-in-the-loop guardrails for high-stakes pricing and financial decisions. • Partner cross-functionally with ML, backend, frontend, data, and business teams to iterate quickly based on feedback and business needs. • Drive technical decisions that directly influence revenue impact, product quality, scalability, and time-to-market. Required Qualifications • Bachelor's degree in Computer Science or a related field. • Experience building recommendation or decisioning systems, ideally in advertising, media, or revenue-platform environments. • Strong understanding of modern LLMs and agentic systems, and the ability to evaluate latency, cost, and quality tradeoffs. • Solid experience with LLM and multi-agent pipelines, including prompting, tool use, orchestration, tradeoff analysis, and error handling. • Experience deploying ML systems in production, including model serving, containerization, CI/CD, and monitoring. • Hands-on experience with modern ML frameworks and tooling such as PyTorch, Hugging Face Transformers, agent orchestration frameworks (e.g., LangGraph or similar), feature stores, and vector databases for RAG workflows. • Experience designing evaluation approaches for recommendation and generative systems using human review, automated and offline metrics, and online A/B testing.</