Senior Software Engineer, Machine Learning

Roku
Bengaluru, IndiaPosted 24 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 team A highly scalable, highly available, real-time advertising platform is critical to support and grow Roku's rapidly expanding ad business. It is the mission of the Ad Engineering Team to build this platform. We are hiring a Senior Machine Learning Engineer, Advertising Platform, to be a vital part of this mission. If you bring extensive experience building complex systems using Machine Learning and Data Science, love great technology at scale, enjoy mentoring, jumping in, and figuring things out in any system, this role is for you! The Ad Serving team, based in Bangalore, is responsible for ensuring that ads on the Roku platform are relevant and of good quality, leading to better outcomes for advertisers and providing a great experience for our viewers. Dealing with millions of ad requests in a day, we need to have best in class ML driven systems to improve the way we serve ads in multiple countries and languages. About the role In this role, you will build and develop ML models to solve for content understanding aspects of Ads. These models rely on the visual, audio and textual content of ads, across multiple languages. This includes developing computer vision and machine learning models/methods such as deep learning models, generative AI solutions, large language models (LLMs) and vision language models (VLMs). You will work with software engineers and other ML engineers to identify, build, train and productionize these models to improve brand safety, increase revenue opportunities and help with moderation and generation of ad creatives in general. What you will be doing Develop scalable and effective machine-learning models and optimization strategies to solve business problems related to Ads Run regular A/B experiments, gather data, and perform statistical analysis Work closely with software engineers and other ML engineers to deliver end-to-end solutions into production Improve the scalability, efficiency and automation of ML driven ad serving systems, model training, deployment and serving Partner with senior stakeholders to plan and drive the ML strategy for Ad Serving We're excited if you have 6+ years of building models for business application experience Master's degree ( or higher) and 4+ years of CS, CE, ML or related field experience Strong CS fundamentals. Should be able to write an algorithm with ease. Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms Good understanding of data science and machine learning fundamentals like regression, classification, tree-based approach, neural networks, sequence-based, and NLP approaches like W2V or Bert Can brainstorm and identify the right KPIs and objective functions. Knowledge of programming languages such as C/C++, Python, Java or Perl (preferred) Have publications at top-tier peer-reviewed conferences or journals (preferred) AI growth mindset / AI Literacy 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 requir ... (truncated, view full listing at source)
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