Senior Machine Learning Engineer
Roku · San Jose, CA · 225 - Advertising Engineering
About this role
Roku is hiring a senior-level Machine Learning Engineer based in San Jose, CA. The posting calls out experience with Deep Learning, Reinforcement Learning, Distributed Systems, Machine Learning. Compensation is listed at $148,750–$361,000 per year.
- Role
- Machine Learning Engineer
- Function
- machine learning
- Level
- senior
- Track
- Individual contributor
- Employment
- Full-time
- Location
- San Jose, CA
- Department
- 225 - Advertising Engineering
More roles at Roku
Job description
from Roku careersTeamwork 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
The Advertising Performance group focuses on performance for all participants in the Advertising ecosystem - Advertisers, Publishers and Roku. The systems and solutions span across different disciplines and technologies to perform realtime multi-objective optimization with distributed systems at large scale and low latencies. We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems and Auction Dynamics to solve a large set of complex problems. At the core of this is our Machine Learning, Experimentation and Inference Platform that powers the entire landscape which we continuously evolve over time.