Staff Machine Learning Engineer, Shopping Ads

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com.

Reddit is a community of communities, built on shared interests, passion, and trust. Our Shopping Ads team builds relevant, performant, and scalable commerce advertising experiences that help advertisers connect products with people who are likely to find them useful.

As a Staff Machine Learning Engineer on Shopping Ads, you will lead the technical strategy and execution for the models that power Shopping Ads delivery. You will work across targeting, retrieval, ranking, engagement and conversion prediction, feature engineering, and online serving to improve advertiser outcomes across Dynamic Product Ads and Product Listing Ads. This is a hands-on technical leadership role for an engineer who can translate business goals into an end-to-end ML roadmap and deliver impact through multiple systems and teams.

Responsibilities

• Lead the ML strategy and architecture for Shopping Ads delivery across targeting, retrieval, ranking, engagement, conversion, and value optimization.
• Own end-to-end model development from opportunity sizing, data and label design, feature engineering, model selection, offline evaluation, online experimentation, deployment, monitoring, and iteration.
• Build and optimize models for low-funnel advertiser objectives while maintaining strong relevance, user experience, marketplace health, and measurement quality.
• Develop feature and representation strategies that connect user intent, context, product catalog signals, advertiser signals, and historical interactions across multiple models in the delivery stack.
• Apply and adapt state-of-the-art machine learning approaches to production problems, selecting architectures based on measurable benefit rather than novelty alone.
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