Senior Software Engineering Manager, Ads Auction & Marketplace Optimization
RokuAustin, TexasPosted 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
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.
About the role
In this role you will build teams focused on the Ads Auction and Marketplace to optimize yield, gross profit, perform realtime multi-objective optimization across various short and long term objectives, build control and simulation systems, design and run experiments and extend the experimentation framework.
You will partner closely with other Engineering, Inventory and Product teams to define the vision and strategy for your group and to build and deliver the roadmap.
What you’ll be doing
Building teams and mentoring engineers across geographic locations and time zones
Defining the strategy and building the long-term roadmap by partnering with Product, Science and Engineering teams
Leading the design, implementation, and evolution of real-time ads auction and marketplace systems
Building and enhancing core auction capabilities including ad pod construction, bid valuation, ranking, pricing, pacing and budget enforcement
Designing auction logic that balances viewer experience (ad load, relevance, repetition) with advertiser performance, publisher yield, platform revenue and gross profit
Driving yield and gross profit optimization through auction design, floor strategies, and pricing experiments
Leading experimentation using A/B testing, auction simulations, offline replay frameworks, and make data-driven decisions based on performance metrics and statistical rigor
Stay at the forefront of advancements in related areas
We’re excited if you have
3+ years of experience leading teams that apply auction theory, quantitative optimization, advanced statistical methods to large, sophisticated datasets
5+ years experience of leading engineering teams from design to implementation, ensuring scalability and reliability
Deep mathematical understanding of auction theory, game theory, economics, quantitative optimization and statistics
Hands-on experience with Python and Java in a production environment
BS or higher in CS, Economics, Mathematics, or a related quantitative field
Preferred Qualifications:
Experience in the Advertising domain
5+ years of experience applying auction theory, game theory, economics, quantitative optimization, advanced statistical methods to large, sophisticated datasets
MS or PhD in CS, Economics, Math or a related quantitative field
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 r ... (truncated, view full listing at source)
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