Machine Learning Engineer

• Apply state of the art ML on search using techniques in deep learning, bandits, transformers, LLMs, causal inference, and optimisations to make our users more delighted and engaged on the platform
• Run online AB tests and analyse them against the critical business KPIs
• Collaborate with US engineering teams as well as cross-functional teams to translate business requirements into technical specifications
• Nurture our ML ecosystem to make it withstand scale, developer velocity and future business shifts
• Provide technical leadership to drive technical and ML roadmap for search ranking and monetisation
• Help in recruiting new engineers. Interview, train, and mentor new team members, Roku fosters an inclusive and collaborative environment where teams generally work in the office Monday through Thursday. Fridays are generally flexible for remote work, except for employees whose specific roles or assigned office location require five days’ a week attendance.

The depth of query, content and user understanding using ML is key to user happiness in their search journey. Solving this customer problem is why we’re actively looking for a Senior Machine Learning Engineer, Search & Recommendations to drive further innovation in search and discovery. The person in this role will leverage their technical skills, business intuition, and analytical thinking to build best-of-class AI-powered products. Communication and presentation skills are important. The role requires both high technical acumen and problem-solving abilities, motivation, and exceptional attention to detail. Every day, you’ll look at what exists and find ways to make it better., * 8+ years of experience (or PhD with 6 years of experience) applying Machine Learning to concrete problems at large-scale in domains like recommendation or search or ads

• Strong CS fundamentals. Should be able to convert ideas to code with ease
• Good understanding of machine learning fundamentals like classification, deep neural nets, and sequence-based models. Familiarity with modern NLP stack and multi-modal representation learning is a plus
• We’d love to see that you’ve worked with big data systems (Spark, S3, and Airflow) and can program (Java, Scala, or Python)
• Good understanding of system architecture. Have experience in big data technologies and streaming architecture, data pipelines, etc.
• Demonstrated AI/agentic-tooling fluency per the section above
• MS in Computer Science, Statistics, or related field, but a Ph.D. in CS or related fields is preferred

At Roku, we don’t just use AI, we work with it. AI agents and smart tools help power drafts, analysis, and repetitive workflows, while our people bring direction, judgment, and accountability. We’re looking for curious, adaptable builders who can show how they’ve used AI or automation to move faster, raise the bar, and scale their impact.

We value your AI skills if you have built fluency across the agentic engineering toolchain - coding harnesses like Claude Code or Cursor, MCP servers, custom skills, or agent frameworks. And you can describe projects where you shipped real work with these tools. You know how to drive an agent, verify its output, and ramp on an unfamiliar codebase with an agent helping you., Roku is a great place for people who want to work in a fast-paced environment where everyone is focused on the company’s success rather than their own. We try to surround ourselves with people who are great at their jobs, who are easy to work with, and who keep their egos in check. We appreciate a sense of humor. We believe a fewer number of very talented folks can do more for less cost than a larger number of less talented teams. We’re independent thinkers with big ideas who act boldly, move fast and accomplish extraordinary things through collaboration and trust. In short, at Roku you’ll be part of a company that’s changing how the world watches TV.

We have a unique culture that we are proud of. We think of ourselves primarily as problem-solvers, which itself is a two-part idea. We come up with the solution, but the solution isn’t real until it is built and delivered to the customer. That penchant for action gives us a pragmatic approach to innovation, one that has served us well since 2002.