Senior Performance Advertising Engineer

Role: Hybrid in Redwood City, CA, or New York, NY (Will consider Remote for the right candidate) Must have: Experience in performance advertising, programmatic bidding, or large-scale user-conversion optimization loops PubMatic is the leading AI-powered ad tech company delivering measurable advertising performance through an intelligent, unified platform that connects buyers, publishers, data partners, and commerce media across CTV, mobile app, and omnichannel environments. About the Role: We are seeking a Senior Performance Advertising Engineer to own and drive the optimization of our buyer-side and performance marketing systems. In this role, you will be directly responsible for designing algorithms, building data systems, and refining optimization loops that drive Return on Ad Spend (ROAS) for our Advertisers and Agencies. You will own the technical architecture and end-to-end evaluation, implementation, and scaling of robust data pipelines for attribution data. This position spans our complete omnichannel environment, requiring expert-level performance engine development across Mobile App (including privacy-aware frameworks like SKAdNetwork and Privacy Sandbox), Video, Connected TV (CTV), and Desktop environments. What You'll Do: ROAS Optimization & Algorithmic Design: Develop, benchmark, and deploy real-time bidding (RTB) algorithms, pacing controls, and predictive models aimed squarely at maximizing ROAS and conversion tracking across all screens (CTV, Mobile App, Video, and Desktop). Attribution Data Pipelines: Design, evaluate, and implement scalable, low-latency streaming and batch data pipelines that ingest, validate, and process complex conversion and multi-touch attribution (MTA) datasets. Omnichannel Execution: Advance PubMatic’s performance capabilities across specific programmatic formats: - Mobile: Build robust mechanisms that thrive within SKAdNetwork, Attribution API, and Android Privacy Sandbox constraints. - CTV & Video: Implement precise cross-device graph integrations, household-level frequency capping, and server-to-server attribution systems.