Senior Software Engineer, Machine learning- Search
RokuBengaluru, IndiaPosted 27 February 2026
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 the role
Our Search Platform sits at the heart of the Roku experience, powering voice, text, and visual discovery across 100M+ active accounts and every Roku-powered device worldwide. We own the entire stack —from ingesting enriching a multi-million-title knowledge graph, to low-latency retrieval services and large-scale machine-learning systems that personalize results in real time. Our work doesn’t just help users find shows; it drives core product surfaces (home-screen rows, browse hubs, Roku Voice, mobile app search) and fuels partner monetization. If you enjoy turning cutting-edge research into products used billions of times a day, you’ll thrive here.
Roku’s footprint has more than quadrupled in the past five years, and user expectations have leaped just as fast—think
LLM-based query understanding, vector-DB retrieval, on-device models, and multimodal search
(voice, text, image). We’re now rebuilding our relevance stack for the next decade, blending classic IR with generative-AI techniques. You will be a technical leader spearheading that transformation.
What
you'll
be doing
Apply state of the art ML on search using techniques in deep learning, bandits, transformers, LLMs, causal inference, and optimizations to make our users more delighted and engaged on the platform
Run online AB tests and analyze 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 monetization
Help recruiting new engineers. Interview, train, and mentor new team members
We're
excited if you have
7+ years of experience (or PhD with 5 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 covert 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
Experience in big data technologies and streaming architecture, data pipelines, etc.
MS in Computer Science, Statistics, or related field, but a Ph.D. in CS or related fields is preferred
AI literacy and curiosity.You have either tried Gen AI in your previous work or outside of work or are curious about Gen AI and have explored it.
#LI-AK1
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.
Benefits
Roku is committed to offering a diverse range of benefits as ... (truncated, view full listing at source)
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