Reddit

Overview

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.

About Reddit

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

Requirements & Eligibility

• 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.

Key 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.