Staff Software Engineer, Demand Bidder, Ad Serving Platform

Roku•New York, NY

9h•$195,000 - $408,000•Hybrid

About The Position

Roku's Ad Serving Team builds and operates the real-time advertising platform that powers all of Roku’s ad monetization across video, display, and native ad formats. Within Ad Serving, the Demand Bidder team owns the high throughput and low latency systems responsible for serving relevant ads for tens of thousands of campaigns through targeting, scoring, bidding, auctions, measurement and more. Every millisecond and every decision carries direct, measurable revenue impact, and the team operates with the high engineering rigor that this demands. We're hiring a Senior Software Engineer (Staff scope) to help scale and evolve this platform. Demand Bidder is a large, high-throughput system driving our next phase of growth. As we onboard a rapid wave of new businesses, we are actively scaling and advancing our architecture — including state of the art pacing engine, scoring/ML engine, modularizing designed for agentic development. Our reliability bar keeps climbing alongside our business scale. We need someone who can rapidly master this intricate ecosystem and take immediate ownership of key initiatives.

Requirements

10+ years of experience designing, building, and operating large-scale, low-latency distributed systems — ideally including at least one system you built substantially from the ground up
A demonstrated track record of owning complex systems end-to-end: design, build, operate, on-call, and iterate based on production feedback
Proven ability to ramp quickly into unfamiliar, complex, large codebases and become productive fast — you enjoy the “figure it out” part of the job as much as the “build it” part
Experience evolving and refactoring live, business-critical systems without compromising uptime or correctness
Strong command of Java, with a solid foundation in algorithms, data structures, concurrency/multi-threading, and performance optimization
Practical experience with distributed caching, high-throughput messaging/streaming systems, and SQL/NoSQL datastores
Comfort operating in cloud infrastructure (GCP or AWS) at scale
B.S. or M.S. in Computer Science, Engineering, or equivalent practical experience
High ownership, self-motivation, critical thinking, and comfort operating with ambiguity
Low-ego and nonpolitical — you'd rather get to the right answer than be the one who's right, and you take critical feedback in stride rather than personally
Genuinely collaborative and team-oriented, with an open mind toward other people's approaches and the outgoing energy to work well across teams
You've used AI coding assistants (e.g., Claude Code, Cursor, or similar) in real production work to accelerate design, implement, debug, and understand unfamiliar codebases. You have informed opinions on how best to leverage
You're curious about applying AI – whether that mean using AI to monitor system health, triage issues or develop agentic workflows
You hold yourself to a high bar of correctness and understanding of what you ship, even as these tools accelerate your pace
Genuine curiosity and a willingness to help shape how the team uses these tools responsibly is what matters most

Nice To Haves

Exposure to applying statistical, control-theory, or ML-driven techniques within production decision-making systems is a strong plus
Real-time bidding / ad-tech domain experience is a big plus, but deep experience in adjacent high-stakes, low-latency domains (trading systems, large-scale ranking/recommendation, matchmaking, risk/fraud systems) is equally valued

Responsibilities

Design, build, and operate core components of a large-scale, real-time distributed decisioning system with strict latency requirements
Take end-to-end ownership of specific subsystems: architecture, implementation, testing, deployment, on-call, and long-term evolution
Build a working mental model of a highly distributes, complex production system, and make high-leverage contributions
Identify opportunities to continuously evolve the system and tooling
Partner with engineers and applied scientists working at the intersection of distributed systems and statistical/ML-driven decision-making
Diagnose and resolve subtle, often timing- or statistics-related issues in a highly concurrent, multi-region distributed system
Mentor other engineers and raise the bar on engineering rigor, testing discipline, and operational excellence across the team
Communicate clearly with both technical and non-technical stakeholders, and drive alignment across engineering, product, and business partners