Staff Machine Learning Engineer, Shopping Ads

🔥 0 minutes ago

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Reddit, Inc.

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2005

💼 Consulting

📣 Marketing

📱 Media

Consulting • Marketing • Media

Reddit, Inc. is a social media platform that acts as a hub for thousands of communities, where users can engage in diverse conversations ranging from breaking news to niche interests. It enables users to post, comment, and vote on content, fostering a vibrant online community. Millions of people globally connect and share their passions on Reddit, creating a dynamic environment for authentic human interaction.

📋 Description

• 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, including 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 relevance, user experience, marketplace health, and measurement quality. • Develop feature and representation strategies connecting user intent, context, product catalog signals, advertiser signals, and historical interactions. • Apply and adapt state-of-the-art machine learning approaches to production problems. • Design systems balancing prediction quality with online latency, throughput, reliability, operational complexity, and serving cost. • Drive complex initiatives across Shopping Ads, Catalog, Foundational Insights, ML Platform, Ads Serving, Auction, Bidding, Product, and Data Science. • Set technical standards through architecture reviews, experimentation standards, production ownership, observability, and model-quality practices. • Mentor engineers and technical leads, clarify ownership, and support execution 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 improving 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 requiring 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 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. • Preferred: experience with Shopping Ads, Commerce ads, Dynamic Product Ads, Product Listing Ads, product recommendation, or retail media. • Preferred: experience with targeting, candidate retrieval, ranking, conversion modeling, value optimization, recommender systems, or representation learning. • Preferred: experience designing features or shared representations across multiple models in a multi-stage delivery stack. • Preferred: experience with deep learning architectures such as multi-task models, sequence models, transformers, two-tower models, graph methods, or learned embeddings. • Preferred: experience with catalog quality, product feeds, advertiser-side signals, delayed or sparse conversion labels, and online/offline distribution shift. • Preferred: experience at a large-scale ads, social, search, recommendation, e-commerce, or marketplace company.

🏖️ Benefits

• Comprehensive Healthcare Benefits and Income Replacement Programs • 401k with Employer Match • Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support • Family Planning Support • Gender-Affirming Care • Mental Health & Coaching Benefits • Flexible Vacation & Paid Volunteer Time Off • Generous Paid Parental Leave • Equity in the form of restricted stock units • Medical, dental, and vision insurance • Generous time off for vacation and parental leave