Sr. Engineering Manager, Shopping Ads

Team & Role Description

The Shopping Ads Engineering team builds the systems that power performant, relevant, and scalable Shopping Ads experiences across Reddit’s Ads platform, including Dynamic Product Ads (DPA) and Product Listing Ads (PLA).

The team focuses on Shopping Ads targeting, retrieval, and engagement modeling to connect advertisers with high-intent users and improve downstream ads performance.

We are looking for a hands-on Engineering Manager to lead this team. This leader will drive technical execution across Shopping Ads systems while remaining deeply involved in architecture, system design, and technical problem solving. The ideal candidate can operate as both an engineering manager and a technical lead manager (TLM), bringing strong ML systems judgment, execution rigor, and the ability to unblock complex technical work.

Responsibilities

• Lead a high-performing team of software and machine learning engineers focused on Shopping Ads targeting, retrieval, ranking, and engagement models
• Drive technical execution for Dynamic Product Ads and Product Listing Ads across ads targeting, candidate retrieval, ranking integrations, model features, and engagement optimization
• Act as a hands-on technical leader by contributing to architecture reviews, technical design, debugging complex production issues, and guiding implementation decisions
• Partner closely with ML Platform, Ads Serving, Product, Data Science, Measurement, and Infrastructure teams to define roadmaps and deliver advertiser impact
• Improve scalability, latency, reliability, relevance, experimentation quality, and operational excellence across Shopping Ads systems
• Drive simplification and platformization efforts that improve developer velocity and reduce operational complexity
• Establish strong engineering practices around system design, experimentation, code quality, observability, and production ownership
• Mentor and develop engineers and technical leads while fostering a culture of technical rigor, accountability, speed, and pragmatic execution
• Identify architectural bottlenecks, technical debt, and scaling risks, and proactively drive solutions across organizational boundaries
• Stay current with industry trends in commerce advertising, recommendation systems, retrieval and ranking architectures, and Shopping Ads optimization

Qualifications

• 8+ years of software engineering or ML engineering experience, including experience leading engineering teams
• Strong hands-on technical depth in ads ranking, retrieval, targeting, recommendation systems, engagement modeling, or ML-driven optimization systems
• Experience building and operating large-scale distributed systems or ML systems in production
• Prov…