Sr. Engineering Manager, Shopping Ads

📋 Description

• • 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 (DPA) and Product Listing Ads (PLA) 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.
• • Operate as a technical lead manager (TLM), balancing leadership responsibilities with direct technical contribution to architecture and system design.
• • Ensure Shopping Ads systems connect advertisers with high-intent users through precise targeting, retrieval, and engagement modeling.
• • Maintain and enhance the performance of large-scale distributed systems and ML systems in production environments.
• • Collaborate cross-functionally to align technical execution with business priorities and advertiser outcomes.
• • Champion best practices in experimentation, observability, and production ownership to ensure system robustness and rapid iteration.
• • Contribute to the evolution of Reddit’s ads platform by integrating cutting-edge advancements in commerce advertising and recommendation architectures.