Staff Machine Learning Engineer, Shopping Ads

Reddit, Inc. is a community of communities built on shared interests, passion, and trust. The Staff Machine Learning Engineer on Shopping Ads will lead the technical strategy for models that enhance Shopping Ads delivery, focusing on improving advertiser outcomes through various machine learning techniques.

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
• Design systems that balance prediction quality with online latency, throughput, reliability, operational complexity, and serving cost
• Drive complex initiatives that require coordinated changes across Shopping Ads, Catalog, Foundational Insights, ML Platform, Ads Serving, Auction, Bidding, Product, and Data Science
• Set a high technical bar through architecture reviews, experimentation standards, production ownership, observability, and model-quality practices
• Mentor engineers and technical leads, clarify ownership, and help the team execute effectively in ambiguous problem spaces
• Stay current with advances in ads optimization, commerce recommendation, retrieval and ranking, representation learning, and production ML systems

Requirements:

• 7+ years of professional software or machine learning engineering experience, including substantial experience building applied ML systems in production
• Demonstrated experience building end-to-end models or model-driven products that improve advertising, recommendation, search, or marketplace performance
• Experience optimizing low-funnel objectives such as conversion, purchase value, revenue, return on ad spend, or other outcome-based metrics
• Strong hands-on experience with model development, complex feature engineering, training and evaluation pipelines, online inference, and experimentation
• Record of delivering complex results that require multiple system components or teams to work together
• Experience applying modern machine learning models in production and producing significant, measurable performance improvements
• Proven technical-lead experience: setting direction, driving architecture and execution, mentoring engineers, and influencing cross-functional stakeholders
• Strong understanding of large-scale, high-throughput, low-latency ML systems and the trade-offs among model quality, latency, reliability, and cost
• Excellent written and verbal communication, mentoring, and collaboration skills, with the ability to align teams on a long-term vision for Shopping Ads delivery
• Experience with Shopping Ads, Commerce ads, Dynamic Product Ads, Product Listing Ads, product recommendation, or retail media
• Experience with one or more of targeting, candidate retrieval, ranking, conversion modeling, value optimization, recommender systems, or representation learning
• Experience designing features or shared representations used across multiple models in a multi-stage delivery stack
• Experience with deep learning architectures such as multi-task models, sequence models, transformers, two-tower models, graph methods, or learned embeddings
• Experience with catalog quality, product feeds, advertiser-side signals, delayed or sparse conversion labels, and online/offline distribution shift
• Experience at a large-scale ads, social, search, recommendation, e-commerce, or marketplace company